ACM DL

ACM Journal on

Emerging Technologies in Computing (JETC)

Menu
Latest Articles

MRAM PUF

In this work, we have studied two novel techniques to enhance the performance of existing geometry-based magnetoresistive RAM physically unclonable function (MRAM PUF). Geometry-based MRAM PUFs rely only on geometric variations in MRAM cells that generate preferred ground state in cells and form the basis of digital signature generation. Here we... (more)

Emerging Technology-Based Design of Primitives for Hardware Security

Hardware security concerns such as intellectual property (IP) piracy and hardware Trojans have... (more)

Spintronic PUFs for Security, Trust, and Authentication

We propose spintronic physically unclonable functions (PUFs) to exploit security-specific properties of domain wall memory (DWM) for security, trust,... (more)

STT-MRAM-Based PUF Architecture Exploiting Magnetic Tunnel Junction Fabrication-Induced Variability

Physically Unclonable Functions (PUFs) are emerging cryptographic primitives used to implement... (more)

A Survey on Chip to System Reverse Engineering

The reverse engineering (RE) of electronic chips and systems can be used with honest and dishonest intentions. To inhibit RE for those with dishonest intentions (e.g., piracy and counterfeiting), it is important that the community is aware of the state-of-the-art capabilities available to attackers... (more)

Frontside Versus Backside Laser Injection

The development of cryptographic devices was followed by the development of so-called implementation attacks, which are intended to retrieve secret information exploiting the hardware itself. Among these attacks, fault attacks can be used to disturb the circuit while performing a computation to retrieve the secret. Among possible means of injecting... (more)

A Fault-Based Secret Key Retrieval Method for ECDSA

Elliptic curve cryptosystems proved to be well suited for securing systems with constrained resources like embedded and portable devices. In a... (more)

Beat Frequency Detector--Based High-Speed True Random Number Generators

True random number generators (TRNGs) are crucial components for the security of cryptographic systems. In contrast to pseudo--random number... (more)

Real-Time Anomaly Detection Framework for Many-Core Router through Machine-Learning Techniques

In this article, we propose a real-time anomaly detection framework for an NoC-based many-core... (more)

Gates vs. Splitters

Optical circuits are considered a promising emerging technology for applications in ultra-high-speed networks or interconnects. However, the development of (automatic) synthesis approaches for such circuits is still in its infancy. Although first generic and automatic synthesis approaches have been proposed, no clear understanding exists yet on how... (more)

NEWS

New JETC Editor-in-Chief

The Journal of Emerging Technologies in Computing Systems is happy to welcome Prof. Yuan Xie (University of California at Santa Barbara as the incoming Editor in Chief! We are also grateful to Prof. Krish Chakrabarty for serving as Editor in Chief for the last six years, and would like to wish to both all the best in their future!

New options for ACM authors to manage rights and permissions for their work

ACM introduces a new publishing license agreement, an updated copyright transfer agreement, and a new author-pays option which allows for perpetual open access through the ACM Digital Library. For more information, visit the ACM Author Rights webpage.

About JETC

Scope

The Journal of Emerging Technologies in Computing Systems invites submissions of original technical papers describing research and development in emerging technologies in computing systems. Major economic and technical challenges are expected to impede the continued scaling of semiconductor devices. This has resulted in the search for alternate mechanical, biological/biochemical, nanoscale electronic, asynchronous and quantum computing and sensor technologies. 

read more

Spintronics: Emerging Ultra-Low Power Circuits and Systems beyond MOS Technology

Guest Editors Introduction: Hardware and Algorithms for On-chip Learning

Alleviate Pin Constraints for Multi-Core Processors Through On/Off-Chip Power Delivery System Design

The number of chip pins is limited due to the cost and reliability issues of sophisticated packages. It is predicted chip pins will be overstretched to satisfy the requirements of power delivery and memory access. The gap will increase as technology scales, due to the increasing computation resources and supply current. Pin reduction techniques are required for continued computing performance growth. In this paper, we propose a chip pin constraint alleviation strategy through on/off-chip power delivery system co-design to effectively reduce the demand for power pins. An analytical model of power delivery system, consisting of on/off-chip regulators and power delivery network, is proposed to evaluate the influence of regulator design and package conduction loss. By combining it with multi-core processor model of performance and memory bandwidth, we characterize the entire system to investigate the chip pin constraint in multi-core processor scaling, and the effectiveness of our strategy. Our strategy achieves a significant pin count reduction, e.g. 31.3% at 8nm technology node. While provided with the same chip pin count, it is able to improve 35.0% chip performance at 8nm, compared to the conventional design. For real applications of different parallelism, our strategy outperforms counterparts with 16.8% performance improvement at 8nm.

Editorial for JETC Special Issue on Alternative Computing Systems

Mobile Unified Memory-Storage Structure based on Hybrid Non-Volatile Memories

In mobile computing systems, the limited amount of main memory space leads to page swap operation overhead and data duplication in both main memory and secondary storage. Furthermore, SQLite write operations in mobile devices such as smartphones and tablet PCs tend to frequently overwrite data to storage, significantly degrading performance. Thus, this paper presents a unified memory-storage structure that is optimized for mobile devices and blurs the boundary between the existing main memory layer and secondary storage layer. The unified memory-storage structure consists of a Dynamic RAM (DRAM) based dual buffering module, hybrid unified memory-storage array consisting of DRAM, a SLC/MLC hybrid 3D cross point array, and NAND Flash memory, and an associated unified storage translation layer devised for the memory address and file translation mechanism as a system software module. This hybrid array of non-volatile memories is formed as a single memory-disk integrated storage space that can be logically divided into static and dynamic spaces. Experimental results show that the overall performance of the hybrid unified memory-storage system with the buffering structure increases by around 59% and power consumption is also improved by 30%, compared to previous integrated memory-disk system.

Distributed In-Memory Computing on Binary RRAM Crossbar

The recent emerging resistive random-access memory (RRAM) can provide non-volatile memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal for low-power and high-throughput data analytics accelerator performed in memory. However, the existing RRAM-crossbar based computing is mainly assumed as a multi-level analog computing, whose result is sensitive to process nonuniformity as well as additional overhead from AD-conversion and I/O. In this paper, we explore the matrix-vector multiplication accelerator on a binary RRAM-crossbar with adaptive 1-bit-comparator based parallel conversion. Moreover, a distributed in-memory computing architecture is also developed with according control protocol. Both memory array and logic accelerator are implemented on the binary RRAM-crossbar, where logic-memory pair can be distributed with protocol of control bus. Experiment results have shown that compared to the analog RRAM-crossbar, the proposed binary RRAM-crossbar can achieve significant area-saving with better calculation accuracy. Moreover, significant speedup can be achieved for matrix-vector multiplication in the neuron-network based machine learning such that the overall training and testing time can be both reduced respectively. In addition, large energy saving can be also achieved when compared to the traditional CMOS-based out-of-memory computing architecture.

Energy-Efficient and Improved Image Recognition with Conditional Deep Learning

Deep learning networks have proven to be very successful for a wide range of recognition tasks across modern computing platforms. However, the computational requirements for such deep nets can be quite high, and hence their energy-efficient implementation is of great interest. Although traditionally the entire network is utilized for the recognition of all inputs, we observe that classification difficulty varies widely across inputs in real-world datasets; only a small fraction of inputs require the full computational effort of a network, while a large majority can be classified correctly with very low effort. In this paper, we propose Conditional Deep Learning (CDL) where convolutional layer features are used to identify the variability in the difficulty of input instances and conditionally activate the deeper layers of the network. The proposed methodology enables the network to dynamically adjust the computational effort depending upon the difficulty of the input data yielding substantial energy savings on MNIST/CIFAR10 datasets. We further employ the conditional approach to train deep learning networks from scratch with integrated supervision from the additional output neurons appended at the convolutional layers. Our proposed integrated CDL training leads to an improvement in the gradient convergence behavior giving substantial error rate reduction on MNIST/CIFAR-10.

Power-Utility-Driven Write Management for MLC PCM

Phase change memory is a promising alternative to DRAM as main memory due to its merits of high density and low leakage power. The Multi-level Cell PCM reveals more attractions than Single-level Cell PCM because it can store multiple bits per cell to achieve higher density. With the iterative write technique, MLC writes demand higher power than DRAM writes, but the power supply of MLC system is similar to that of DRAM. The incompatibility of high write power and limited power budget results in the degradation of the write throughput and performance. In this work, we investigate both write scheduling policy and power management to improve the MLC power utility and alleviate the negative impacts. We identify the power-utility-driven write scheduling as an online bin-packing problem and then derive a power-utility-driven scheduling (PUDS) policy from the First-Fit algorithm to improve the write power usage. Based on the SET ramp-down pulse characteristic, we propose the SET Power Amortization (SPA) policy which proactively reclaims the power tokens at intra-SET level to promote the power utilization. Our results demonstrate that the system with PUDS+SPA has a 60% increase of performance and 36% improvement of the power utility over the state-of-the-art power management technique.

Design of approximate compressors for multiplication

Approximate computing has recently developed as a promising technique for energy efficient VLSI system design and also best suited for error resilient applications, such as signal processing and multimedia. Approximate computing reduces accuracy, but it still provides significant and faster results with usually lower power consumption. This is mostly attractive for arithmetic circuits. In this paper, various novel design approaches of approximate 4-2 and 5-2 Compressors are proposed for reduction of the partial products stages during multiplication. Three approximate 8x8 Dadda multiplier designs using a novel three 4-2 approximate compressors and also two approximate 8x8 Dadda multiplier designs using a novel 5-2 approximate Compressors are proposed. Extensive simulation results show that the proposed designs achieve significant accuracy improvement together with power and delay reductions compared to previous approximate designs.

Survey of STT-MRAM Cell Design Strategies: Taxonomy and Sense Amplifier Tradeoffs for Resiliency

Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed herein. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, sense margin, and energy or power consumption costs versus resiliency benefits. Solutions to the reliability issues identified are addressed within a taxonomy created to categorize the current and future approaches to reliable STT-MRAM designs. A variety of destructive and non-destructive sensing schemes are assessed for process variation tolerance, read disturbance reduction, sense margin, and write polarization asymmetry compensation. The highest resiliency strategies deliver a sensing margin above 300 mV while incurring low power and energy consumption on the order of picojoules and microwatts, respectively, while attaining read sense latency of a few nanoseconds down to hundreds of picoseconds for non-destructive and destructive sensing schemes, respectively. Additional Key Words and Phrases: Spin-Transfer Torque storage elements, STT-MRAM, Magnetic Tunnel Junction (MTJ), Self-referencing schemes, Reliability, Process Variation, Read/Write Reliability

Structured Pruning of Deep Convolutional Neural Networks

Real time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses. Network pruning is a promising technique to solve this problem. However, pruning usually results in irregular network connections that not only demand extra representation efforts but also do not fit well on parallel computation. We introduce structured sparsity at various scales for convolutional neural networks, which are feature map wise, kernel wise and intra kernel strided sparsity. This structured sparsity is very advantageous for direct computational resource savings on embedded computers, parallel computing environments and hardware based systems. To decide the importance of network connections and paths, the proposed method uses a particle filtering approach. The importance weight of each particle is assigned by assessing the misclassification rate with corresponding connectivity pattern. The pruned network is re-trained to compensate for the losses due to pruning. While implementing convolutions as matrix products, we particularly show that intra kernel strided sparsity with a simple constraint can significantly reduce the size of the kernel and feature map tensors. The proposed work shows that when pruning granularities are applied in combinations, we can prune the network by more than 70% with less than 1% loss.

Memory-Centric Reconfigurable Accelerator for Classification and Machine Learning Applications

Big Data refers to the growing challenge of turning massive, often unstructured datasets into meaningful, actionable data. As datasets grow from petabytes to exabytes and beyond, it becomes increasingly difficult to run advanced analytics, especially machine learning, in a reasonable time and on a practical power budget. Previous work has focused on accelerating analytics implemented as SQL queries on data-parallel platforms with off-the-shelf CPUs and GPGPUs. However, these systems are general-purpose, and still require a vast amount of data transfer between storage and computing elements, limiting system efficiency. Instead, we present a reconfigurable, memory-centric accelerator which operates at the last level of memory, dramatically reducing the energy required for data transfer and processing of machine learning applications. We functionally validate the framework using a hardware emulation platform and three representative applications: Naive Bayesian Classification, Convolutional Neural Networks, and k-Means Clustering. Results are compared with implementations on a modern CPU and GPU. Finally, the use of in-memory dataset decompression to further reduce data transfer volume is investigated. The system achieves an average energy efficiency improvement of 74x and 212x over GPU and single-threaded CPU, respectively, while dataset compression is shown to improve overall efficiency by an additional 1.8x on average.

Real Time SoC Security against Passive Threats using Crypsis Behaviour of Geckos

The rapid evolution of the embedded era has witnessed globalization for the design of SoC architectures in the semiconductor design industry. Though issues of cost and complexity have been resolved in such a methodology, yet the root of hardware trust have been evicted. Malicious circuitry, a.k.a. Hardware Trojan Horses (HTH) is inserted by adversaries in the untrusted phases of design. HTH remains dormant during testing but gets triggered at runtime to cause sudden active and passive attacks. In this work, we focus on the runtime passive threats posed by HTH. Nature inspired algorithms offers an alternative to the conventional techniques for solving complex problems in the domain of computer science. However, most are optimization techniques and none is dedicated to security. We seek refuge to the crypsis behavior exhibited by geckos to generate a runtime security technique for SoC architectures, which can bypass runtime passive threats of HTH. An intelligent security IP which works on the proposed security principles is designed based on the structure of ART1 neural architecture. The security mechanism is demonstrated with the aid of Finite State Automata. Low area and power overhead of our proposed security IP over standard benchmarks and practical crypto SoC architectures as obtained in experimental results supports its applicability for practical implementations.

High-Performance Computing with Quantum Processing Units

The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for functional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loose integration that assumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. We also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.

Exploiting Idle Hardware to provide Low Overhead Fault Tolerance for VLIW Processors

Because of technology scaling, the soft error rate has been increasing in digital circuits, which affects system reliability. Therefore, modern processors, including VLIW architectures, must have means to mitigate such effects to guarantee reliable computing. In this scenario, our work proposes three low overhead fault tolerance approaches based on instruction duplication with zero latency detection, which uses a rollback mechanism to correct soft errors in the pipelanes of a configurable VLIW processor. The first uses idle issue slots within a period of time to execute extra instructions considering distinct application phases. The second works at a finer grain, adaptively exploiting idle functional units at run-time. However, some applications present high ILP (instruction-level parallelism), and so the ability to provide fault tolerance is reduced: less functional units will be idle, decreasing the number of potential duplicated instructions. The third approach attacks this issue by dynamically reducing ILP according to a configurable threshold, increasing fault tolerance at the cost of performance. While the first two approaches achieve significant fault coverage with minimal area and power overhead for applications with low ILP, the latter improves fault tolerance with low performance degradation. All approaches are evaluated considering area, performance, power dissipation, and error coverage.

SPARCNet: A Hardware Accelerator for Efficient Deployment of Sparse Convolutional Networks

Deep neural networks have been shown to outperform prior state-of-the-art solutions that often relied heavily on hand-engineered feature extraction techniques coupled with simple classification algorithms. In particular, deep convolutional neural networks have been shown to dominate on several popular public benchmarks such as ImageNet database. Unfortunately, the benefits of deep networks have yet to be fully exploited in embedded, resource-bound settings that have strict power and area budgets. In order to reduce power and area while still achieving required throughput, classification-efficient network architectures are required in addition to optimal deployment on efficient hardware. In this work, we target both of these enterprises. For the first objective, we analyze simple, biologically-inspired reduction strategies that are applied both before and after training. The central theme of the techniques is the introduction of sparsification to help dissolve away the dense connectivity that is often found at different levels in convolutional networks. In the second contribution, we propose SPARCNet: a hardware accelerator for efficient deployment of SPARse Convolutional NETworks. The accelerator looks to enable deploying networks in such resource-bound settings by exploiting efficient forms of parallelism and the proposed sparsification techniques.

Monolayer Transistor SRAMs: Towards Low-Power, Denser Memory Systems

Monolayer heterojunction FETs based on vertical transition metal dichalcogenides and planar black phosphorus FETs (BPFETs) have demonstrated excellent subthreshold swing, high ION/IOFF, and high scalability, making them attractive candidates for post-CMOS memory design. This paper explores TMDCFET and BPFET SRAM design by combining atomistic self-consistent device modeling with SRAM circuit design and simulation. We perform detailed evaluations of the TMDCFET/BPFET SRAMs at a single bitcell and at SRAM array level. Our simulations show that at low operating voltages, TMDCFET/BPFET SRAMs exhibit significant advantages in static power, dynamic read/write noise margin, and read/write delay over nominal 16nm CMOS SRAMs at both bitcell and array level implementations.

Sketching Computation with Stochastic Processing Engines

In conventional embedded computing, a sudden shortage of computing resource, such as premature termi-nation or power outage, often results a complete computing failure and produces totally unusable results.To circumvent this challenge, we present a novel technique that allows reconfigurable computing to achieve quality scalability by leveraging probabilistic principle. Our objective is to maximize the quality and us-ability of final results even under sudden change of computing resource.This paper explores how to leverage stochastic principle to gracefully salvage partially finished results of embedded computing. Our work is inspired by the concept of incremental sketching frequently found in artistic rendering, where the drawing procedure consists of a series of steps, each gradually improving the quality of results. The essence of our approach is to encode the input signal as the probability density function, perform stochastic computing operations on the signal in the probabilistic domain, and decode the output signal by estimating the probability density function of the resulting random samples.To validate our proposed architecture design, we have implemented a proof-of-concept probabilistic convolver with a Virtex 6FPGA device. Finally, we use three convolution-based image processing applications, image correspondence,image sharpening, and edge detection, to demonstrate that important embedded computing applications can indeed be sketched in a graceful manner.

Energy Neutral Design Framework for Supercapacitor-based Autonomous Wireless Sensor Networks

To design autonomous Wireless Sensor Networks (WSNs) with a theoretical infinite lifetime, energy harvesting (EH) techniques have been recently considered as promising approaches. In this paper, an efficient energy harvesting system compatible with various environmental sources such as light, heat or wind energy is proposed. Our platform takes advantage of double-level capacitors not only to prolong the system lifetime but also to enable robust booting from the exhausting energy of the system. Simulations and experiments show that our Multiple Energy Sources Converter (MESC) can achieve booting time in order of seconds. Although capacitors have virtual recharge cycles, they suffer from higher leakage compared to rechargeable batteries. Increasing their size can decrease the system Quality of Service (QoS) due to leakage energy. Therefore, an energy neutral design framework providing a methodology to determine the minimum size of the storage devices satisfying Energy Neutral Operation (ENO) and maximizing QoS in EH nodes when using a given energy source is proposed. Experiments validating this framework are performed on a real WSN platform with both photovoltaic cells and thermal generators in an indoor environment. Moreover, simulations on OMNET++ show that the energy storage optimized from our design framework is utilized up to 93.86%.

Towards Human-Scale Brain Computing Using 3D Wafer Scale Integration

Stochastic CBRAM based Neuromorphic Time Series Prediction System

In this research, we present a CBRAM (conductive-bridge RAM) based neuromorphic system which efficiently addresses time series prediction. We propose a new (i) voltage-mode stochastic multi-weight synapse circuit based on experimental bi-stable CBRAM devices, (ii) a voltage-mode neuron circuit based on the concept of charge sharing, and (iii) an optimized training methodology powered by a stochastic implementation of the least-mean-squares (SLMS) training rule. To validate the proposed design, we use time series prediction for short-term electrical load forecasting in smart grids. Our system is able to forecast hourly electrical loads with a mean accuracy of 96%, an estimated power dissipation of 15 µW and area of 14.5 µm2 at 65 nm CMOS technology

Computing Polynomials using Unipolar Stochastic Logic

This paper addresses subtraction and polynomial computations using unipolar stochastic logic. Stochastic computing requires simple logic gates and stochastic logic based circuits are inherently fault-tolerant. While it is easy to realize multiplication and scaled addition, implementation of subtraction is non-trivial using unipolar stochastic logic. Additionally, an accurate computation of subtraction is critical for the implementation of polynomials with negative coefficients in stochastic unipolar representation. This paper, for the first time, demonstrates that instead of using well-known Bernstein polynomials, stochastic computation of polynomials can be implemented by using a stochastic subtractor and factorization. Three major contributions are made in this paper. First, two approaches are proposed to compute subtraction in stochastic unipolar representation. In the first approach, the subtraction operation is approximated by cascading multi-levels of OR and AND gates. In the second approach, the stochastic subtraction is implemented using a multiplexer and a stochastic divider. Second, computation of polynomials in stochastic unipolar format is presented using scaled addition and proposed stochastic subtraction. Third, we propose stochastic computation of polynomials using factorization. From experimental results, it is shown that the proposed stochastic logic circuits require less hardware complexity than the previous stochastic polynomial implementation using Bernstein polynomials.

PPU: A Control Error-Tolerant Processor for Streaming Applications with Formal Guarantees

Current error-tolerant processors allow errors in the computation, and are positioned to be suitable for error-tolerant applications such as media applications. For such processors, the Instruction-Set-Architecture (ISA) no longer serves as a specification, since it is acceptable for the processor to allow for errors during the execution of instructions. In this work, we address this specification gap by defining the minimal requirements that are needed in order for an error-tolerant processor to provide useful results. Further, we formally define properties that capture these requirements. Based on this, we propose YMM, an error-tolerant processor that aims to meet these requirements with low-cost microarchitectural support. These protection mechanisms convert potentially fatal control errors to potentially tolerable data errors instead of ensuring instruction-level or byte-level correctness. The protection mechanisms in YMM protect the system against crashes, unresponsiveness, and external device corruption. In addition, they also provide support for achieving acceptable result quality. Additionally, we provide a methodology that formally proves the specification properties on YMM using model checking. This methodology uses models for the hardware and software that are integrated with the fault and recovery models. Finally, we experimentally demonstrate the results of model checking and the application-level quality of results for YMM.

One-step Sneak-path Free Read Scheme for Resistive Crossbar Memory

A one-step sneak-path free read scheme for resistive crossbar memory is proposed in this paper. During read operation, it configures the crossbar memory array into a 4-terminal resistance network, which is comprised of the resistor of selected cell and three other resistors corresponding to the unselected cells that contribute to the sneak-path. Two sensing voltages with equal potential are applied to three terminals of the network. One is for sensing the resistance of the selected memory cell; the other is for creating zero voltage drop across one of the three resistors, which connects the sneak-path to the selected cell. This effectively suppresses the current injected by the sneak-path to the selected cell sensing loop. This work also proposes a cost-effective data encoding circuit that guarantees at lease half of the memory cells are in high-resistance state, which further minimizes sneak-path current. The impact of key design parameters, such as sensing voltage, switch on-resistance, and the ratio of memory cell resistances in different states, as well as non-ideal effect, e.g. amplifier offset voltage, are investigated. Equations for estimating the maximum size of crossbar array to share a single read circuit are derived. The effectiveness of the proposed design has been validated and studied via circuit simulations.

Redesign the Memory Allocator for Non-Volatile Main Memory

The non-volatile memoryNVM has the merits of byte-addressability, fast speed, persistency and low power consumption, which make it attractive to be used as main memory. Commonly, user process dynamically acquires memory through memory allocators. However, traditional memory allocators designed with in-place data writes are not appropriate for non-volatile main memoryNVRAM due to the limited endurance. In this paper, first, we quantitatively analyze the wear-oblivious of DRAM-oriented designed allocatorglibc malloc and the inefficiency of wear-conscious allocatorNVMalloc. Then, we propose WAlloc, an efficient wear-aware manual memory allocator designed for NVRAM: (1) decouples metadata and data management; (2) distinguishes metadata with volatility; (3) redirects the data writes around to achieve wear-leveling; (4) redesigns an efficient and effective NVM copy mechanism, bypassing the CPU cache and prefetching data explicitly. Finally, experimental results show that the wear-leveling of WAlloc outperforms that of NVMalloc about 30% and 60% under random workloads and well-distributed workloads, respectively. Besides, WAlloc reduces average data memory writes in 64 bytes block by an average of 1.5X comparing with glibc malloc. With the fulfillment of data persistency, cache bypassing NVM copy is better than clflushing NVM copy with performance of circa 14% improvement.

VLSI Architecture for the Restricted Boltzmann Machine

Neural network (NN) systems are widely used in many important applications ranging from computer vision to speech recognition. To date, most NN systems are processed by general processing units like CPUs or GPUs. However, as the sizes of dataset and network rapidly increase, the original software implementations suffer from long training time. To overcome this problem, specialized hardware accelerators are needed to design high-speed NN systems. This paper presents an efficient hardware architecture of restricted Boltzmann machine (RBM) that is an important category of NN systems. Various optimization approaches at hardware level are performed to improve the training speed. As-soon-as-possible and overlapped-scheduling approaches are used to reduce the latency. It is shown that, compared with the flat design, the proposed RBM architecture can achieve 50% reduction in training time. In addition, an on-the-fly computation scheme is also used to reduce the storage requirement of binary and stochastic states by several hundreds of times. Then, based on the proposed approach, a 784-2252 RBM design example is developed for MNIST handwritten digit recognition dataset. Analysis shows that the VLSI design of RBM achieves 170 times speedup in training as compared to a CPU-based solution with small performance loss.

Trading Accuracy for Energy in Stochastic Circuit Design

As we approach the end of Moores Law, alternative computing techniques that consume energy more efficiently have been proposed. Stochastic computing (SC) is a re-emerging computing technique that acts on data encoded by bit-streams. It is a low-cost and error-tolerant alternative to conventional binary circuits in some important applications. This paper presents an accuracy-energy tradeoff technique for SC circuits that reduces their energy consumption with virtually no accuracy loss. To this end, we employ voltage/frequency scaling, which normally reduces energy consumption at the cost of timing errors. Then we show that due to their inherent error tolerance, SC circuits operate satisfactorily without significant accuracy loss even with aggressive scaling. Furthermore, we find that logical and physical design techniques can be combined to expand the already powerful accuracy-energy tradeoff possibilities of SC. In particular, we demonstrate that careful adjustment of path delays can lead to error reduction under voltage/frequency scaling. Simulation results show that our optimized SC circuits can tolerate aggressive voltage scaling with no significant SNR degradation after 40% supply voltage reduction (1V to 0.6V), leading to 66% energy saving (20.7pJ to 6.9pJ). We also show that process variation and temperature variation have limited impact on optimized SC circuits.

Guest Editorial: Special Issue on Nanoelectronic Circuit and System Design Methods for the Mobile Computing Era

Bibliometrics

Publication Years 2005-2016
Publication Count 321
Citation Count 737
Available for Download 321
Downloads (6 weeks) 1978
Downloads (12 Months) 15389
Downloads (cumulative) 134838
Average downloads per article 420
Average citations per article 2
First Name Last Name Award
Iris Bahar ACM Distinguished Member (2012)
Krishnendu Chakrabarty ACM Distinguished Member (2008)
ACM Senior Member (2006)
Nikil D. Dutt ACM Distinguished Member (2007)
Igor Markov ACM Distinguished Member (2011)
ACM Senior Member (2007)
Dharmendra Modha ACM Gordon Bell Prize
Special Category (2009) ACM Gordon Bell Prize
Special Category (2009)
Saraju P. Mohanty ACM Senior Member (2010)
Trevor Mudge ACM-IEEE CS Eckert-Mauchly Award (2014)
Massoud Pedram ACM Distinguished Member (2008)
Steven K Reinhardt ACM Distinguished Member (2010)

First Name Last Name Paper Counts
Niraj Jha 19
Krishnendu Chakrabarty 10
Wei Zhang 7
Michael Niemier 6
Rodney Van Meter 6
Yuan Xie 6
Kaushik Roy 6
Li Shang 6
Xiaobosharon Hu 5
Partha Pande 5
Mehdi Tahoori 5
Fabrizio Lombardi 5
Mariagrazia Graziano 4
Pierre Gaillardon 4
Shuo Wang 4
Lei Wang 4
Alvin Lebeck 4
Mohammad Tehranipoor 4
Chris Dwyer 4
Morteza Zamani 3
Spyros Tragoudas 3
Mahdi Nikdast 3
Michael Crocker 3
Paul Wettin 3
Jordi Cortadella 3
Sourindra Chaudhuri 3
Jianwei Dai 3
Mehdi Sedighi 3
Aoxiang Tang 3
Yaoyao Ye 3
Bhargab Bhattacharya 3
Tao Xu 3
Jacques Klein 3
Weisheng Zhao 3
Fei Su 3
Ferdinand Peper 3
Arun Ravindran 3
Nagarajan Ranganathan 3
Maurizio Zamboni 3
Eren Kursun 3
Robert Wille 3
Arindam Mukherjee 3
Xiaowen Wu 3
Rolf Drechsler 3
Kishor Trivedi 3
Bao Liu 2
Massoud Pedram 2
Jacob Murray 2
Oliver Keszocze 2
Saibal Mukhopadhyay 2
John Savage 2
Byungsoo Choi 2
Damien Querlioz 2
Rivalino Matias 2
Sparsh Mittal 2
Xuanyao Fong 2
Ashok Palaniswamy 2
Lei Wang 2
Jing Huang 2
André DeHon 2
Suman Datta 2
Tsungyi Ho 2
Mehrdad Nourani 2
Min Chen 2
Alexis De Vos 2
Faquir Jain 2
Ruth Bahar 2
Anil Wipat 2
Mahboobeh Houshmand 2
Shashikanth Bobba 2
Pallav Gupta 2
Marco Ottavi 2
Bharat Joshi 2
Josep Carmona 2
Philippe Coussy 2
Baris Taskin 2
Xianmin Chen 2
Mona Arabzadeh 2
Giovanni De Micheli 2
Arighna Deb 2
Jaidev Patwardhan 2
Luca Schiano 2
Yongtae Kim 2
Prateek Mishra 2
Ulf Schlichtmann 2
Amlan Ganguly 2
Eric Rachlin 2
Vijaykrishnan Narayanan 2
Peng Li 2
Hafizur Rahaman 2
Dhiraj Pradhan 2
Torben Mogensen 2
Behrooz Shirazi 2
Xuan Wang 2
Anand Raghunathan 2
Rangharajan Venkatesan 2
Vijay Reddy 2
Sumeet Gupta 2
Mostafizur Rahman 2
Csaba Moritz 2
Sachin Sapatnekar 2
Himanshu Thapliyal 2
Jiang Xu 2
Frederic Chong 2
Siddhartha Datta 2
Mehdi Saeedi 2
Chiachun Lin 2
Djaafar Chabi 2
Mrigank Sharad 2
Reza Rad 2
Santosh Khasanvis 2
Douglas Densmore 2
Chris Myers 2
Sudip Roy 2
Kolin Paul 2
Stefano Frache 2
Yaojun Zhang 2
Giovanni Micheli 2
Cheng Zhuo 2
Mike Hayes 1
Rajeevan Amirtharajah 1
Justin Wenck 1
Damiano Piovesan 1
Enrico Macii 1
Jos Vanderleyden 1
Kathleen Marchal 1
Wujie Wen 1
Vladimir Nikitin 1
Daniel Lottis 1
Kiseok Moon 1
Daniel Mange 1
Oana Boncalo 1
H Wong 1
Kwangting Cheng 1
Margot Damaser 1
Martin Arlitt 1
David Du 1
Tao Yang 1
Tridib Mukherjee 1
Hafiz Sheikh 1
Ishfaq Ahmad 1
Landon Sego 1
Manish Vachharajani 1
Mark Cianchetti 1
David Albonesi 1
Chialin Yang 1
Alain Pegatoquet 1
Olivier Berder 1
K Habib 1
Saber Moradi 1
Daniel Fasnacht 1
Leo Filippini 1
Houle Gan 1
Tanay Karnik 1
Milad Maleki 1
Srihari Cadambi 1
Nobuaki Miyakawa 1
Huaixiu Zheng 1
Z Wang 1
Curtis Madsen 1
Ashutosh Chakraborty 1
Xuehui Zhang 1
Christopher Curtis 1
Yuchun Ma 1
Haera Chung 1
Keran Zhou 1
Chris Kim 1
Andres Kwasinski 1
Gerardo Pelosi 1
Yu Bi 1
Jiannshiun Yuan 1
Carlotta Guiducci 1
Leyla Nazhandali 1
Naoya Tate 1
Shengqi Yang 1
Robert Hannon 1
Jamil Wakil 1
Yue Wu 1
Daniel Davids 1
John Hayes 1
Aditya Prasad 1
Jun Zeng 1
Mariam Momenzadeh 1
Graham Jullien 1
Rajeswari Devadoss 1
S Wong 1
Jiale Liang 1
Elena Maftei 1
Kele Shen 1
Sarmishtha Ghoshal 1
Jing Xie 1
Benoit Chappet De Vangel 1
César Torres-Huitzil 1
Cyrille Chavet 1
Nikil Dutt 1
Pooria Yaghini 1
Nader Bagherzadeh 1
Chiahung Chien 1
Marco Tartagni 1
Dongjae Shin 1
Minkyu Maeng 1
Luis Plana 1
David Clark 1
Jim Garside 1
Eustace Painkras 1
Marc Jaekel 1
Evan Lent 1
Domenico Cotroneo 1
Jin Sun 1
Vandi Alves 1
Sumeet Gupta 1
Yihang Chen 1
Anja Von Beuningen 1
Mehdi Kamal 1
Luca Ramini 1
Domenic Forte 1
Sina Shahbazmohamadi 1
Marco Indaco 1
Sanjukta Bhanja 1
Kae Nemoto 1
Simeranjit Brar 1
Jiang Xu 1
Steven Rubin 1
Gilda Garretón 1
Sujay Deb 1
Deukhyoun Heo 1
Ketan Patel 1
Nabanita Majumdar 1
Gabriel Schulhof 1
Wei Zhao 1
Nadine Gergel-Hackett 1
Yuxing Yao 1
Prachi Joshi 1
Yungchih Chen 1
Dong Xiang 1
Jifeng Chen 1
Renu Kumawat 1
Jiunli Lin 1
Amlan Chakrabarti 1
Bernard Girau 1
Laurent Rodriguez 1
Hrishikesh Jayakumar 1
Woosuk Lee 1
Zhongqi Li 1
Aldo Romani 1
Nahid Hossain 1
Chinghwa Cheng 1
Avinash Kodi 1
Victor Nicola 1
D Miller 1
Mathias Soeken 1
Bernard Diény 1
Fang Liu 1
Chandan Sarkar 1
Nong Xiao 1
Bipul Paul 1
Bryan Jackson 1
Charles Augustine 1
Hai Li 1
Andy Tyrrell 1
Andrew Greensted 1
Joël Rossier 1
Virgile Javerliac 1
Kotb Jabeur 1
Stephane Gros 1
Pierre Paoli 1
Chengwen Wu 1
Keqin Li 1
Wei Chen 1
Liang Wen 1
Xiong Pan 1
Ney Calazans 1
Jamie Collier 1
Clemens Moser 1
Rita Casadio 1
Ramprasad Ravichandran 1
Bulent Kurdi 1
Dharmendra Modha 1
Geoffrey Burr 1
Sri Choday 1
Yiran Chen 1
Jun Yang 1
Alexey Khvalkovskiy 1
Mary Eshaghian-Wilner 1
Lucian Prodan 1
Mihai Udrescu 1
Gordon Wan 1
Jacob White 1
Tsungching Huang 1
Aditya Bansal 1
Paul Falkenstern 1
Mohamad Sawan 1
Dang Nguyen 1
Steve Majerus 1
Zhenyu Sun 1
Amip Shah 1
Radu Marculescu 1
Sajal Das 1
Michal Lipson 1
Keren Bergman 1
Hongyu Zhou 1
Holger Axelsen 1
Greg Snider 1
Trung Nguyen 1
Azzurra Pulimeno 1
Gefei Wang 1
Youguang Zhang 1
Kiyoung Choi 1
William Fornaciari 1
Vivek De 1
Ayse Coskun 1
Ruiyu Wang 1
Massimo Roch 1
Roger Lake 1
Zhaohao Wang 1
Yifang Liu 1
Yang Yi 1
Shankar Balachandran 1
Veezhinathan Kamakoti 1
Martin Roetteler 1
Franjo Ivančić 1
Yong Zhan 1
Natsuo Nakamura 1
Taeho Kgil 1
Dan Venutolo 1
Erik Lindgren 1
Jennifer Hallinan 1
Harold Fellermann 1
Zhiqiang Li 1
Bibhash Sen 1
Anuroop Vidapalapati 1
Guangyu Sun 1
Masoud Zamani 1
Huazhong Yang 1
Tingting Hwang 1
Matthias Beste 1
Junlin Chen 1
Mark Tehranipoor 1
Swaroop Ghosh 1
Yier Jin 1
Amey Kulkarni 1
Darshan Thaker 1
Kouichi Akahane 1
Daniel Sorin 1
Alexander Khitun 1
Mircea Vlăduţiu 1
Lukáš Sekanina 1
Huaiyuan Tseng 1
Yujie Huang 1
Krishna Kant 1
Yaohong Wang 1
Giacomo Ghidini 1
Tahir Cader 1
Andrew Rawson 1
William Gustafson 1
Aleksandr Biberman 1
Qianfan Xu 1
Alan Mickelson 1
Bipul Paul 1
Masaki Okajima 1
Pinghung Yuh 1
Eva Rotenberg 1
Ismo Hänninen 1
Craig Lent 1
Ryangary Kim 1
Olivier Sentieys 1
Mostafa Azghadi 1
Mehmet Ozdas 1
Edith Beigné 1
Niraj Jha 1
Alaeddin Aydiner 1
Chenyuan Zhao 1
Chidhambaranathan R 1
Chirag Garg 1
Arnab Roy 1
Cesare Ferri 1
Sherief Reda 1
Steven Reinhardt 1
Jonathan Salkind 1
Qiaoyan Yu 1
Ernst Oberortner 1
Tara Deans 1
Chris Winstead 1
Fatima Hadjam 1
Hanwu Chen 1
Joseph Horton 1
Andrew Ferraiuolo 1
Hanieh Mirzaei 1
Bo Yuan 1
Bin Li 1
Swarup Bhunia 1
El Hasaneen 1
Glenn Reinman 1
Shigeto Nakayama 1
Nathan Binkert 1
Trevor Mudge 1
David Wolpert 1
Paul Ampadu 1
Nicholas Roehner 1
Goksel Misirli 1
Rudolf Füchslin 1
Herbert Sauro 1
Biplab Sikdar 1
Mark Hagan 1
Daniel Grissom 1
Nishad Nerurkar 1
Jie Chen 1
Sandro Bartolini 1
Elena Vatajelu 1
Anirudh Iyengar 1
Kaveh Shamsi 1
Xunzhao Yin 1
Jayita Das 1
Christine Nardini 1
Pratik Kabali 1
Weichen Liu 1
Yao Xu 1
Ransford Hyman 1
Yang Liu 1
Vasilis Pavlidis 1
Debasis Mitra 1
Narayanan Komerath 1
Fiona Teshome 1
Gabriel Loh 1
Lloyd Harriott 1
Arthur Nieuwoudt 1
Sezer Gören 1
Jia Lee 1
Behnam Ghavami 1
Manoj Gaur 1
Luke Pierce 1
Ozan Ozbag 1
Juha Plosila 1
Hannu Tenhunen 1
Oluleye Olorode 1
Nilanjan Goswami 1
Michele Dini 1
Minhao Zhu 1
Suman Sah 1
Benjamin Belzer 1
Vivek Shende 1
Jonathan Bean 1
Okan Palaz 1
Weiguo Tang 1
S Srinivasan 1
Mohsen Raji 1
Hossein Pedram 1
Shunming Syu 1
Rodney Meter 1
Syyen Kuo 1
Marc Galceran-Oms 1
John Bainbridge 1
Aaron Dingler 1
Stefano Russo 1
Senthil Arasu 1
Fumio Machida 1
Jean Araujo 1
Paulo Maciel 1
William Cane-Wissing 1
Muhammad Ahsan 1
Loic Decloedt 1
Angsuman Sarkar 1
Hang Zhang 1
Woomin Hwang 1
Marco Vacca 1
Jeffrey Pepper 1
M Amadou 1
Philippe Matherat 1
Ian O'Connor 1
John Jr 1
Haldun Kufluoglu 1
Xueqing Li 1
Xun Gao 1
Davide Bertozzi 1
Hoda Khouzani 1
Fabrice Bernard-Granger 1
Gregory Pendina 1
Meghna Mankalale 1
Lei Wang 1
Zhiguang Chen 1
José Abellán 1
A Goud 1
Giuseppe Profiti 1
Pier Martelli 1
Andrea Acquaviva 1
Arijit Raychowdhury 1
Alvaro Padilla 1
Simone Raoux 1
Sayeef Salahuddin 1
Satish Kumar 1
Xueti Tang 1
Xiao Luo 1
Shiva Navab 1
Kang Wang 1
Albert Lin 1
Pedro Irazoqui 1
Chunyi Lee 1
Wen Ko 1
Muhammad Salam 1
Michael Suster 1
Paul Fletter 1
Hsinhung Liao 1
Tao Wang 1
Xiang Chen 1
Prithviraj Banerjee 1
Alan Savage 1
Siddharth Garg 1
Diana Marculescu 1
Andrès Márquez 1
Jacob Levy 1
Michael Thomsen 1
Kamalika Datta 1
Umamaheswara Tida 1
Pragyan Mohanty 1
Yiyu Shi 1
Tiansheng Zhang 1
Yue Zhang 1
Claude Chappert 1
Sungjun Yoon 1
V Devanathan 1
Yuchun Ma 1
Yongxiang Liu 1
Eiri Hashimoto 1
Yutaka Sacho 1
Ali Saidi 1
Andy Chiu 1
Haiyao Huang 1
Claudio Moraga 1
Marek Perkowski, 1
Xiaoyu Song 1
Md Rahman 1
Gerhard Dueck 1
Samik Some 1
Olivier Thomas 1
Yu Cao 1
Yu Wang 1
Howie Huang 1
Alessandro Barenghi 1
Shahed Quadir 1
John Chandy 1
Mario Barbareschi 1
Paolo Prinetto 1
Jaewon Jang 1
Chengwei Lin 1
Giovanni De Micheli 1
Tzvetan Metodi 1
Andrew Cross 1
Michael Henry 1
Jeyavijayan Rajendran 1
Ashok Srivastava 1
Krishnendu Chakrabarty 1
Benjamin Gojman 1
Venkataraman Mahalingam 1
Clay Mayberry 1
Xuemei Chen 1
Kerry Bernstein 1
James Tour 1
Garrett Rose 1
Peter Kogge 1
Zahra Sasanian 1
H Ugurdag 1
Meng Zhang 1
Kenichi Morita 1
H Wong 1
V Kamakoti 1
A Bhattacharya 1
Soumya Eachempati 1
Yuan Xie 1
Jaeyoon Kim 1
Spyros Tragoudas 1
Pohsun Wu 1
Niraj Jha 1
Syed Jafri 1
Misagh Khayambashi 1
Vijay Raghunathan 1
Tao Li 1
Jue Wang 1
Miguel Lastras-Montano 1
Melika Payvand 1
Kwangting Cheng 1
Matteo Filippi 1
Dongjin Kim 1
Rajit Manohar 1
Fabien Clermidy 1
Roberto Pietrantuono 1
Jing Zhao 1
Yanbin Wang 1
Gautam Kapila 1
Jack Sampson 1
Abbas Dehghani 1
Kamal Jamshidi 1
Jianyu Chen 1
Sylvain Claireux 1
Guangyan Zhang 1
Xuhao Chen 1
Ajay Joshi 1
Xia Zhang 1
Wensi Wang 1
Terence O'Donnell 1
Elisa Ficarra 1
Rohit Shenoy 1
Bipin Rajendran 1
Subho Chatterjee 1
Alexander Driskill-Smith 1
André Stauffer 1
Pierre Mudry 1
Gianluca Tempesti 1
Marya Lieberman 1
Jie Deng 1
Chenpang Kung 1
Swaroop Ghosh 1
Steven Garverick 1
Yaojoe Yang 1
Diana Franklin 1
Sandeep Gupta 1
Nicolas Sherwood-Droz 1
Kyle Preston 1
Gilbert Hendry 1
William Hwang 1
Stéphane Burignat 1
Tetsuo Yokoyama 1
Alireza Shafaei 1
Trongnhan Le 1
Arnaud Carer 1
Wang Kang 1
Davide Zoni 1
Andrew Kahng 1
Rajat Chakraborty 1
Takanori Maebashi 1
Krisztiàn Flautner 1
Dennis Huo 1
Zhen Zhang 1
Maik Hadorn 1
Perrine Batude 1
Shengqi Yang 1
Wenping Wang 1
Wei Zhang 1
Pinaki Mazumder 1
Christof Teuscher 1
Ali Afzali-kusha 1
Luca Breveglieri 1
Jie Zhang 1
Qianying Tang 1
Keshab Parhi 1
Giorgio Natale 1
Lionel Torres 1
Youngok Pino 1
Matthew French 1
Kohei Itoh 1
Isaac Chuang 1
Harika Manem 1
Minlun Chuang 1
Mark Oskin 1
Ashwani Sharma 1
Aravinda Kar 1
Jin He 1
Kevin Chang 1
Xinmin Yu 1
M Balakrishnan 1
Michael Leuchtenburg 1
Csaba Moritz 1
Pavan Panchapakeshan 1
Jan Madsen 1
Sandip Tiwari 1
Weichen Liu 1
Vineet Sahula 1
Yang Du 1
Bertrand Granado 1
Ahmed Hemani 1
Nasim Farahini 1
Ashkan Eghbal 1
Amirali Ghofrani 1
Luke Theogarajan 1
Chulmin Kim 1
Kyuho Park 1
Hyunchul Seok 1
Alex Yakovlev 1
Simon Davidson 1
Jun Pang 1
Steve Furber 1
Steve Temple 1
Nor Haron 1
Said Hamdioui 1
Roberto Natella 1
Roman Lysecky 1
Janet Roveda 1
Qian Wang 1
Juinndar Huang 1
Jungsang Kim 1
Yuan Xue 1
Chengmo Yang 1
Guillaume Prenat 1
Debesh Das 1
Arnab Biswas 1
Anand Raghunathan 1
Matheus Moreira 1
Peter Beerel 1
Chung Lam 1
Gregory Corrado 1
Roger Cheek 1
Charles Rettner 1
Wengfai Wong 1
Chengkok Koh 1
R Williams 1
Dmytro Apalkov 1
Penli Huang 1
Hai Li 1
Yiran Chen 1
Vlasia Anagnostopoulou 1
Georgios Varsamopoulos 1
Hengxing Tan 1
Moustafa Mohamed 1
Thomas Lee 1
Shinobu Fujita 1
Stijn De Baerdemacker 1
Jie Meng 1
Giacomo Indiveri 1
Can Sitik 1
Emre Salman 1
Suzanne Lesecq 1
Jinho Lee 1
Kyungsu Kang 1
Naser MohammadZadeh 1
Weikai Shih 1
Bryant Wysocki 1
Nathan McDonald 1
Muzaffer Simsir 1
Jason Cong 1
Shinjiro Toyoda 1
Jie Chen 1
Natalio Krasnogor 1
Amlan Gangul 1
Jude Rivers 1
Philip Brisk 1
Fuwei Chen 1
Saeed Safari 1
Vineeth Vijayakumaran 1
Manoj Yuvaraj 1
Paolo Grani 1
Guido Bertoni 1
Stefano Sanfilippo 1
Ruggero Susella 1
Yingjie Lao 1
Navid Asadizanjani 1
Kenneth Ramclam 1
Kevin Scott 1
Stefan Hillmich 1
William Munro 1
Chunyao Wang 1
Garrett Rose 1
Makoto Naruse 1
Naokatsu Yamamoto 1
Motoichi Ohtsu 1
Hu Xu 1
Bryan Black 1
Douglas Tougaw 1
Yang Zhao 1
Mircea Stan 1
Timothy Dysart 1
Pritish Narayanan 1
Michael Gladshtein 1
Jeffrey Krichmar 1
Yong Zhang 1
Benoît Miramond 1
Hugues Wouafo 1
Siddharth Gaba 1
Seongmin Kim 1
Basit Sheikh 1
Michael Kishinevsky 1
Delong Shang 1
Claude Cirba 1
Cathy Chancellor 1
Ahmed Louri 1
Rubens Matos 1
F De Souza 1
Lungyen Chen 1
Laurent Becker 1
Kalyan Biswas 1
Ke Jiang 1
Ramy Tadros 1
Ningning Wang 1
Lothar Thiele 1
Brendan O'Flynn 1
Cian O'Mathuna 1
Jeff Siebert 1
Jianjia Chen 1
Piero Fariselli 1
Matthew Breitwisch 1
Kailash Gopalakrishnan 1
Niladri Mojumder 1
Adrian Ong 1
Eugene Chen 1
Bruce Tidor 1
Jing Li 1
Muthukumar Murugan 1
Zahra Abbasi 1
Sanjay Ranka 1
Phanisekhar Bv 1
Kevin Fox 1
Christopher Mundy 1
Johnnie Chan 1
Zheng Li 1
Yaowen Chang 1
Robert Glück 1
Indranil Sengupta 1
Xiang Wei 1
Gianluca Piccinini 1
Dafine Ravelosona 1
Tayebeh Bahreini 1
Lu Wang 1
Martin Barke 1
Clare Thiem 1
Milan Patnaik 1
Q Shi 1
Swapnil Bhatia 1
Manojit Dutta 1
Woohyung Lee 1
Wulong Liu 1
Naseef Mansoor 1
Guru Venkataramani 1
Teng Lu 1
H Wong 1
Subhasish Mitra 1
Zhehui Wang 1
Tinoosh Mohsenin 1
James Donald 1
Xiaojun Ma 1
Jeremy Tolbert 1
Tadashi Kawazoe 1
Jiale Huang 1
Mukta Farooq 1
Charles Lieber 1
Igor Markov 1
Konrad Walus 1
Yu Cao 1
Adam Cabe 1
Kushal Datta 1
Yehia Massoud 1
Ajay Bhoj 1
Jorge Kina 1
ChiOn Chui 1
Chunyao Wang 1
Stanley Yeh 1
Paul Pop 1
Zhehui Wang 1
Ingchao Lin 1
Laura Conde-Canencia 1
Arnab Raha 1
Wei Lu 1
Alexander Gotmanov 1
Xuefu Zhang 1
Fei Xia 1
Masud Chowdhury 1
Junchen Liu 1
Gabriela Nicolescu 1
Yuliang Jin 1
Karthik Shankar 1
Moonseok Kim 1
Christophe Layer 1
Abdullah Guler 1
Chao Chen 1
Wei Jiang 1
Zoha Pajouhi 1
Ajay Singhvi 1
Taşkın Koçak 1
Saraju Mohanty 1
Siva Narendra 1
Francesco Abate 1
Sungkyu Lim 1
Lyn Venken 1
Alejandro Schrott 1
Steven Watts 1
Mohamad Krounbi 1
Yousuke Takada 1
Shihhsien Kuo 1
Xiaoxia Wu 1
Anish Muttreja 1
Yang Liu 1
Shriram Raghunathan 1
Sheyshi Lu 1
Chandrakant Patel 1
Cullen Bash 1
Susmit Biswas 1
Heba Saadeldeen 1
Ricardo Bianchini 1
Raymond Beausoleil 1
Xi Chen 1
Gaurav Rathi 1

Affiliation Paper Counts
Universite Pierre et Marie Curie 1
MCKV Institute of Engineering 1
University of Victoria 1
Feng Chia University 1
Southeast University China, Nanjing 1
Google Inc. 1
Yangzhou University 1
University of Kansas 1
Centre Hospitalier de L'Universite de Montreal 1
The University of British Columbia 1
Samsung Group 1
State University of New York at New Paltz 1
Ohio University Athens 1
Nanzan University 1
University of Texas at Austin 1
Brno University of Technology 1
Indian Institute of Science 1
University of Waterloo 1
University of Maryland, Baltimore 1
Peking University 1
University of Washington 1
Defence Research and Development Organisation India 1
University of Washington Seattle 1
Japan Science and Technology Agency 1
Zurich University of Applied Sciences Winterthur 1
Yeditepe University 1
National University of Singapore 1
George Mason University 1
Advanced Micro Devices, Inc. 1
Harbin Institute of Technology 1
Chang Gung University 1
Hewlett-Packard 1
University of Twente 1
Chongqing University 1
University of North Texas 1
University of California, Berkeley 1
Valparaiso University 1
University of Oxford 1
Federal University of Piaui 1
Cadence Design Systems 1
Research Organization of Information and Systems National Institute of Informatics 1
Wuhan University 1
Chung Yuan Christian University 1
University of California, San Diego 1
Rutgers, The State University of New Jersey 1
Hiroshima University 1
University of Copenhagen 1
University of California System 1
NEC Corporation 1
Utah State University 1
Universite Nice Sophia Antipolis 1
Texas Instruments (India) Ltd 1
National Taiwan University Hospital 1
Amazon.com, Inc. 1
Ozyegin University 1
ORT Braude - College of Engineering 1
ARM Ltd. 1
Kalyani Government Engineering College 1
Polytechnic University - Brooklyn 1
University of Calgary 2
University of Siena 2
Harbin Engineering University 2
Delft University of Technology 2
University of Turku 2
University of Missouri-Kansas City 2
Commissariat a L'Energie Atomique CEA 2
Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpellier LIRMM 2
Harvard University 2
Qualcomm Incorporated 2
Seoul National University 2
Federal University of Uberlandia 2
University of Southern California, Information Sciences Institute 2
Kirtland Air Force Base 2
Pontifical Catholic University of Rio Grande do Sul 2
Shahed University 2
Louis Stokes Cleveland VA Medical Center 2
University of Science and Technology of China 2
University of Bristol 2
Jadavpur University 2
Bahcesehir University 2
Johannes Kepler University Linz 2
STMicroelectronics 2
Oak Ridge National Laboratory 2
Southern Illinois University 2
University of Ferrara 2
Missouri University of Science and Technology 2
Daneshgahe Esfahan 2
Stony Brook University 2
Virginia Tech 2
University of Seoul 2
Oracle Corporation 2
Carnegie Mellon University 2
University of Alberta 2
California Institute of Technology 2
Hefei National Laboratory for Physical Sciences at Microscale 2
Universite de Lyon 2
Toshiba America Research, Inc 2
European Centre for Soft Computing 2
Universite de Lorrain 2
Indian Institute of Technology 2
George Washington University 3
Indian Institute of Technology, Kharagpur 3
Louisiana State University 3
Technical University of Denmark 3
National Chiao Tung University Taiwan 3
Beihang University 3
University of York 3
University of New Brunswick 3
Malaviya National Institute of Technology 3
NEC Laboratories America, Inc. 3
Air Force Research Laboratory 3
University of Tehran 3
University of Delaware 3
Indian Statistical Institute, Kolkata 3
University of Maryland, Baltimore County 3
Catholic University of Leuven 3
Indian Institute of Technology, Delhi 3
Polytechnic University of Timisoara 4
Tyndall National Institute at National University of Ireland, Cork 4
Royal Institute of Technology 4
University of Texas at Arlington 4
Universite de Bretagne-Sud 4
University of Electronic Science and Technology of China 4
Nanyang Technological University 4
University of Minnesota System 4
CEA LETI 4
University of Arizona 4
Polytechnic School of Montreal 4
Technical University of Munich 4
IBM 4
Shanghai University 4
Portland State University 4
University of Tokyo 4
University of Rochester 4
Columbia University 4
Universite de Rennes 1 4
Federal University of Pernambuco 4
Ghent University 4
Karlsruhe Institute of Technology 4
National Institute of Technology, Durgapur 4
Rice University 5
Universitat Politecnica de Catalunya 5
University of Texas at Dallas 5
University of Florida 5
University of Naples Federico II 5
National Tsing Hua University 5
Texas A and M University 5
University of Central Florida 5
Massachusetts Institute of Technology 5
Pacific Northwest National Laboratory 5
University of Utah 5
University of Minnesota Twin Cities 5
University of California, Riverside 5
Japan National Institute of Information and Communications Technology 5
Case Western Reserve University 6
University of Texas at San Antonio 6
University of California, Irvine 6
University of California, Davis 6
National Cheng Kung University 6
University of Pittsburgh 6
New York University 6
University of Virginia 6
Politecnico di Milano 6
Universite Paris-Sud XI 6
University of Southern California 6
Southern Illinois University at Carbondale 6
Arizona State University 7
Newcastle University, United Kingdom 7
Drexel University 7
National University of Defense Technology China 7
Indian Institute of Technology, Madras 7
University of Manchester 8
Keio University 8
Korea Advanced Institute of Science & Technology 8
The Institute of Fundamental Electronics, Orsay 8
Rochester Institute of Technology 9
IBM Almaden Research Center 9
HP Labs 9
Brown University 9
Tsinghua University 9
Swiss Federal Institute of Technology, Zurich 9
University of California, Los Angeles 9
National Taiwan University 10
Cornell University 10
Texas Instruments 10
IBM Thomas J. Watson Research Center 10
University Michigan Ann Arbor 10
Bremen University 11
University of Massachusetts Amherst 11
University of Bologna 11
University of Colorado at Boulder 12
The University of North Carolina at Charlotte 12
Georgia Institute of Technology 13
Boston University 13
Swiss Federal Institute of Technology, Lausanne 13
University of South Florida Tampa 14
Stanford University 14
Amirkabir University of Technology 15
Northeastern University 16
University of California, Santa Barbara 16
Intel Corporation 17
Hong Kong University of Science and Technology 18
Pennsylvania State University 19
Washington State University 20
Polytechnic Institute of Turin 21
University of Notre Dame 23
University of Connecticut 30
Purdue University 31
Duke University 32
Princeton University 45

ACM Journal on Emerging Technologies in Computing Systems (JETC) - Special Issue on Secure and Trustworthy Computing
Archive


2016
Volume 13 Issue 1, December 2016 Special Issue on Secure and Trustworthy Computing
Volume 13 Issue 2, November 2016  Issue-in-Progress
Volume 12 Issue 4, July 2016 Regular Papers

2015
Volume 12 Issue 3, September 2015 Special Issue on Cross-Layer System Design and Regular Papers
Volume 12 Issue 2, August 2015 Special Issue on Advances in Design of Ultra-Low Power Circuits and Systems in Emerging Technologies
Volume 12 Issue 1, July 2015
Volume 11 Issue 4, April 2015 Special Issues on Neuromorphic Computing and Emerging Many-Core Systems for Exascale Computing

2014
Volume 11 Issue 3, December 2014 Special Issue on Computational Synthetic Biology and Regular Papers
Volume 11 Issue 2, November 2014 Special Issue on Reversible Computation and Regular Papers
Volume 11 Issue 1, September 2014
Volume 10 Issue 4, May 2014
Volume 10 Issue 3, April 2014
Volume 10 Issue 2, February 2014
Volume 10 Issue 1, January 2014 Special Issue on Reliability and Device Degradation in Emerging Technologies and Special Issue on WoSAR 2011

2013
Volume 9 Issue 4, November 2013 Special Issue on Bioinformatics
Volume 9 Issue 3, September 2013
Volume 9 Issue 2, May 2013 Special issue on memory technologies
Volume 9 Issue 1, February 2013

2012
Volume 8 Issue 4, October 2012
Volume 8 Issue 3, August 2012
Volume 8 Issue 2, June 2012 Special Issue on Implantable Electronics
Volume 8 Issue 1, February 2012

2011
Volume 7 Issue 4, December 2011
Volume 7 Issue 3, August 2011
Volume 7 Issue 2, June 2011
Volume 7 Issue 1, January 2011

2010
Volume 6 Issue 4, December 2010
Volume 6 Issue 3, August 2010
Volume 6 Issue 2, June 2010
Volume 6 Issue 1, March 2010

2009
Volume 5 Issue 4, November 2009
Volume 5 Issue 3, August 2009
Volume 5 Issue 2, July 2009
Volume 5 Issue 1, January 2009

2008
Volume 4 Issue 4, October 2008
Volume 4 Issue 3, August 2008
Volume 4 Issue 2, April 2008
Volume 4 Issue 1, March 2008
Volume 3 Issue 4, January 2008

2007
Volume 3 Issue 3, November 2007
Volume 3 Issue 2, July 2007
Volume 3 Issue 1, April 2007

2006
Volume 2 Issue 4, October 2006
Volume 2 Issue 3, July 2006
Volume 2 Issue 2, April 2006
Volume 2 Issue 1, January 2006

2005
Volume 1 Issue 3, October 2005
Volume 1 Issue 2, July 2005
Volume 1 Issue 1, April 2005
 
All ACM Journals | See Full Journal Index

Search JETC
enter search term and/or author name