Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems

Rajendran, Bipin; Liu, Yong; Seo, Jae-sun; Gopalakrishnan, Kailash; Chang, Leland; Friedman, Daniel; Ritter, Mark B.

doi:10.1109/ted.2012.2227969

Cited by 142 publications

(104 citation statements)

References 16 publications

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“…A 3-layer network is capable of accuracies, on previously unseen 'test' images (generalization), of ∼97% [4] (Fig.4); even higher accuracy is possible by first "pre-training" the weights in each layer [5]. Like STDP, low-power neurons should be achievable by emphasizing brief spikes [7] and local-only clocking.…”

Section: Figmentioning

confidence: 99%

Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

Burr

Shelby

Nolfo

et al. 2014

2014 IEEE International Electron Devices Meeting

193

177

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Using 2 phase-change memory (PCM) devices per synapse, a 3-layer perceptron network with 164,885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for NVM+selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2% (82.9%). Using a neural network (NN) simulator matched to the experimental demonstrator, extensive tolerancing is performed with respect to NVM variability, yield, and the stochasticity, linearity and asymmetry of NVM-conductance response.

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Section: Figmentioning

confidence: 99%

Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

Burr

Shelby

Nolfo

et al. 2014

2014 IEEE International Electron Devices Meeting

193

177

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“…Specifically, researchers have demonstrated similarities between memristive networks and swarm intelligence algorithms [8][9][10][11][12]. Swarm intelligence is the collaborative behavior of decentralized self-organized agents.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Pajouhi

Roy

2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers to solve a maze in hardware. In this paper, we utilize swarm intelligence of memristive networks to perform image edge detection. First, we propose a hardware-friendly algorithm for image edge detection based on ant colony optimization. Second, we implement the image edge detection algorithm using memristive networks. Furthermore, we explain the impact of various parameters of the memristors on the efficacy of the implementation. Our results show 28% improvement in the energy compared to a low power CMOS hardware implementation based on stochastic circuits. Furthermore, our design occupies up to 5x less area.

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“…1(b), was recently proposed as a promising solution for learning in hardware neural networks [7] [8]. The iterative solution to the sparse coding problem can be realized by mapping the matrix D onto the resistive array, and learning takes place through the update step.…”

Section: Introductionmentioning

confidence: 99%

Neurophysics-inspired parallel architecture with resistive crosspoint array for dictionary learning

Kadetotad¹,

Xu²,

Mohanty³

et al. 2014

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

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This paper proposes a parallel architecture with resistive crosspoint array. The design of its two essential operations, Read and Write, is inspired by the biophysical behavior of a neural system, such as integrate-and-fire and timedependent synaptic plasticity. The proposed hardware consists of an array with resistive random access memory (RRAM) and CMOS peripheral circuits, which perform matrix product and dictionary update in a fully parallel fashion, at the speed that is independent of the matrix dimension. The entire system is implemented in 65nm CMOS technology with RRAM to realize high-speed unsupervised dictionary learning. As compared to state-of-the-art software approach, it achieves more than 3000X speedup, enabling real-time feature extraction on a single chip.

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Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems

Cited by 142 publications

References 16 publications

Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Neurophysics-inspired parallel architecture with resistive crosspoint array for dictionary learning

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