Dataset Information

Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks.

ABSTRACT: Spiking Neural Networks (SNNs) offer great potential to promote both the performance and efficiency of real-world computing systems, considering the biological plausibility of SNNs. The emerging analog Resistive Random Access Memory (RRAM) devices have drawn increasing interest as potential neuromorphic hardware for implementing practical SNNs. In this article, we propose a novel training approach (called greedy training) for SNNs by diluting spike events on the temporal dimension with necessary controls on input encoding phase switching, endowing SNNs with the ability to cooperate with the inevitable conductance variations of RRAM devices. The SNNs could utilize Spike-Timing-Dependent Plasticity (STDP) as the unsupervised learning rule, and this plasticity has been observed on our one-transistor-one-resistor (1T1R) RRAM devices under voltage pulses with designed waveforms. We have also conducted handwritten digit recognition task simulations on MNIST dataset. The results show that the unsupervised SNNs trained by the proposed method could mitigate the requirement for the number of gradual levels of RRAM devices, and also have immunity to both cycle-to-cycle and device-to-device RRAM conductance variations. Unsupervised SNNs trained by the proposed methods could cooperate with real RRAM devices with non-ideal behaviors better, promising high feasibility of RRAM array based neuromorphic systems for online training.

SUBMITTER: Guo Y

PROVIDER: S-EPMC6691091 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks.

Guo Yilong Y Wu Huaqiang H Gao Bin B Qian He H

Frontiers in neuroscience 20190806

Spiking Neural Networks (SNNs) offer great potential to promote both the performance and efficiency of real-world computing systems, considering the biological plausibility of SNNs. The emerging analog Resistive Random Access Memory (RRAM) devices have drawn increasing interest as potential neuromorphic hardware for implementing practical SNNs. In this article, we propose a novel training approach (called greedy training) for SNNs by diluting spike events on the temporal dimension with necessary ...[more]

PMID: 31447634

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Project description:Spiking neural networks (SNNs) are a computational tool in which the information is coded into spikes, as in some parts of the brain, differently from conventional neural networks (NNs) that compute over real-numbers. Therefore, SNNs can implement intelligent information extraction in real-time at the edge of data acquisition and correspond to a complementary solution to conventional NNs working for cloud-computing. Both NN classes face hardware constraints due to limited computing parallelism and separation of logic and memory. Emerging memory devices, like resistive switching memories, phase change memories, or memristive devices in general are strong candidates to remove these hurdles for NN applications. The well-established training procedures of conventional NNs helped in defining the desiderata for memristive device dynamics implementing synaptic units. The generally agreed requirements are a linear evolution of memristive conductance upon stimulation with train of identical pulses and a symmetric conductance change for conductance increase and decrease. Conversely, little work has been done to understand the main properties of memristive devices supporting efficient SNN operation. The reason lies in the lack of a background theory for their training. As a consequence, requirements for NNs have been taken as a reference to develop memristive devices for SNNs. In the present work, we show that, for efficient CMOS/memristive SNNs, the requirements for synaptic memristive dynamics are very different from the needs of a conventional NN. System-level simulations of a SNN trained to classify hand-written digit images through a spike timing dependent plasticity protocol are performed considering various linear and non-linear plausible synaptic memristive dynamics. We consider memristive dynamics bounded by artificial hard conductance values and limited by the natural dynamics evolution toward asymptotic values (soft-boundaries). We quantitatively analyze the impact of resolution and non-linearity properties of the synapses on the network training and classification performance. Finally, we demonstrate that the non-linear synapses with hard boundary values enable higher classification performance and realize the best trade-off between classification accuracy and required training time. With reference to the obtained results, we discuss how memristive devices with non-linear dynamics constitute a technologically convenient solution for the development of on-line SNN training.

Dataset Information

Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks.

Publications

Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks.

Similar Datasets

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