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Introduction to Special Issue on Neuromorphic Computing
Dan Hammerstrom, Vijaykrishnan Narayanan
Article No.: 32
Toward a Sparse Self-Organizing Map for Neuromorphic Architectures
Laurent Rodriguez, Benoît Miramond, Bertrand Granado
Article No.: 33
Neurobiological systems have often been a source of inspiration for computational science and engineering, but in the past their impact has also been limited by the understanding of biological models. Today, new technologies lead to an equilibrium...
On-Chip Universal Supervised Learning Methods for Neuro-Inspired Block of Memristive Nanodevices
Djaafar Chabi, Weisheng Zhao, Damien Querlioz, Jacques-Olivier Klein
Article No.: 34
Scaling down beyond CMOS transistors requires the combination of new computing paradigms and novel devices. In this context, neuromorphic architecture is developed to achieve robust and ultra-low power computing systems. Memristive nanodevices are...
Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories
Philippe Coussy, Cyrille Chavet, Hugues Nono Wouafo, Laura Conde-Canencia
Article No.: 35
Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered...
Large-Scale Spiking Neural Networks using Neuromorphic Hardware Compatible Models
Jeffrey L. Krichmar, Philippe Coussy, Nikil Dutt
Article No.: 36
Neuromorphic engineering is a fast growing field with great potential in both understanding the function of the brain, and constructing practical artifacts that build upon this understanding. For these novel chips and hardware to be useful,...
Bio-inspired neural computation attracts a lot of attention as a possible solution for the future challenges in designing computational resources. Dynamic neural fields (DNF) provide cortically inspired models of neural populations to which...
A Reconfigurable Digital Neuromorphic Processor with Memristive Synaptic Crossbar for Cognitive Computing
Yongtae Kim, Yong Zhang, Peng Li
Article No.: 38
This article presents a brain-inspired reconfigurable digital neuromorphic processor (DNP) architecture for large-scale spiking neural networks. The proposed architecture integrates an arbitrary number of N digital leaky integrate-and-fire...
Special Issue on Emerging Many-Core Systems for Exascale Computing
Masoud Daneshtalab, Farhad Mehdipour, Zhiyi Yu, Hannu Tenhunen
Article No.: 39
Architecture and Implementation of Dynamic Parallelism, Voltage and Frequency Scaling (PVFS) on CGRAs
Syed M. A. H. Jafri, Ozan Ozbag, Nasim Farahini, Kolin Paul, Ahmed Hemani, Juha Plosila, Hannu Tenhunen
Article No.: 40
In the era of platforms hosting multiple applications with arbitrary performance requirements, providing a worst-case platform-wide voltage/frequency operating point is neither optimal nor desirable. As a solution to this problem, designs...
Improving Performance in Sub-Block Caches with Optimized Replacement Policies
Oluleye Olorode, Mehrdad Nourani
Article No.: 41
Recent advances in computer processor design have led to the introduction of sub-blocking to cache architectures. Sub-block caches reduce the tag area and power overhead in caches without reducing the effective cache size by using fewer tags to...
iConn: A Communication Infrastructure for Heterogeneous Computing Architectures
Zhongqi Li, Nilanjan Goswami, Tao Li
Article No.: 42
Recently, the graphics processing unit (GPU) has made significant progress as a general-purpose parallel processor. The CPU and GPU cooperate together to solve data-parallel and control-intensive real-world applications in an optimized...
Analytical Reliability Analysis of 3D NoC under TSV Failure
Misagh Khayambashi, Pooria M. Yaghini, Ashkan Eghbal, Nader Bagherzadeh
Article No.: 43
The network-on-chip (NoC) technology allows for integration of a manycore design on a single chip for higher efficiency and scalability. Three-dimensional (3D) NoCs offer several advantages over two-dimensional (2D) NoCs....