Improving Spiking Neural Network Accuracy Using Time-based Neurons
| Autor: | Hanseok Kim, Woo-Seok Choi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Quantitative Biology::Neurons and Cognition FOS: Electrical engineering electronic engineering information engineering Computer Science - Neural and Evolutionary Computing Neural and Evolutionary Computing (cs.NE) Electrical Engineering and Systems Science - Signal Processing |
| Popis: | Due to the fundamental limit to reducing power consumption of running deep learning models on von-Neumann architecture, research on neuromorphic computing systems based on low-power spiking neural networks using analog neurons is in the spotlight. In order to integrate a large number of neurons, neurons need to be designed to occupy a small area, but as technology scales down, analog neurons are difficult to scale, and they suffer from reduced voltage headroom/dynamic range and circuit nonlinearities. In light of this, this paper first models the nonlinear behavior of existing current-mirror-based voltage-domain neurons designed in a 28nm process, and show SNN inference accuracy can be severely degraded by the effect of neuron's nonlinearity. Then, to mitigate this problem, we propose a novel neuron, which processes incoming spikes in the time domain and greatly improves the linearity, thereby improving the inference accuracy compared to the existing voltage-domain neuron. Tested on the MNIST dataset, the inference error rate of the proposed neuron differs by less than 0.1% from that of the ideal neuron. Accepted in ISCAS 2022 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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