| 研究実績の概要 |
This year, we developed and verified the following technologies for the hybrid-driven reconfigurable perception-computation platform: (1) Spike coding of Electroencephalogram (EEG) signals and its spiking neural network (SNN)-based processing. In several works, we successfully applied spike coding to adaptive, stochastic and frequency coding of EEG signals, respectively, and achieved competitive sleep stage classification accuracy based on SNN; (2) A ternary weight quantization method for deep SNNs and hardware implementation. In this work, we achieved energy-efficient inference hardware by quantizing the weights of SNNs to {-1, 0, 1}. The gradient disappearance problem during model training is avoided by designing cross-layer connections. Simple logical operations can be used in ternary weights SNNs at the inference stage, to reducing hardware overhead; (3) Training and construction mechanism of reconfigurable bisection neural network (BNN) topology. We proposed a general construction method of BNN and its training mechanism. By constructing a mask matrix with a bisection structure, we can automatically train a BNN model with a specific topology.
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| 今後の研究の推進方策 |
Firstly, we will integrate SNN with bisection topology to realize reconfigurable SNN hardware. Then, the adders and multipliers in the original SNN hardware are replaced with look-up tables to realize low-power calculations. Secondly, we will explore the integration of stochastic computing and BNN to realize a computing architecture with temporal-spatial re-configurability. Finally, we apply the proposed platform to various online perception/computation applications.
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