Abstract | ||
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Memristive spiking neural networks (MSNNs) have great potential to process information with higher efficiency and lower time latency than conventional artificial neural networks (ANNs). However, MSNNs still lack effective hardware-based training algorithms to achieve comparable performance to the mature ANNs. Therefore, a multilayer locally-connected (LC) MSNN is proposed to realize high performance with self-adaptive and in-situ learning. In the LC-MSNN, spatial and temporal interactions are introduced to activate hidden neurons spontaneously; synaptic weights are updated locally with spike-time-dependent plasticity (STDP) by pulse scheme including processing and updating phases; nonlinear conductance response (CR) is utilized to realize the adjustive learning rate. The LC-MSNN is comprehensively verified and benchmarked with the MNIST dataset. Moreover, self-adaptive activations of the hidden neurons are investigated by extracting and visualizing their internal states and related features; the adjustive learning rate is studied in different nonlinear CR. Effects of non-idealities including finite resolution, device-to-device variation, and yield, are also taken into consideration in the LC-MSNN. Simulation results show the LC-MSNN has better performance (maximum recognition rate of 97.4%) and robustness to non-idealities. Therefore, this method is a hardware-friendly algorithm and can be applied to realize high-performance SNNs in a memristor-based hardware system. |
Year | DOI | Venue |
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2021 | 10.1016/j.neucom.2021.08.011 | Neurocomputing |
Keywords | DocType | Volume |
Memristor,SNNs,Locally-connected,Self-adaptive,In-situ,Adjustive learning rate,STDP | Journal | 463 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jiwei Li | 1 | 2 | 2.40 |
Hui Xu | 2 | 12 | 7.67 |
Shengyang Sun | 3 | 2 | 3.75 |
Zhiwei Li | 4 | 1315 | 107.73 |
Qingjiang Li | 5 | 6 | 4.87 |
Haijun Liu | 6 | 2 | 4.43 |
Nan Li | 7 | 28 | 28.52 |