论文标题
使用基于时间的神经元提高尖峰神经网络精度
Improving Spiking Neural Network Accuracy Using Time-based Neurons
论文作者
论文摘要
由于减少对von-Neumann体系结构的深度学习模型的功耗的基本限制，因此使用模拟神经元基于低功率尖峰神经网络的神经形态计算系统的研究引起了人们的关注。为了整合大量神经元，需要设计神经元以占据很小的区域，但是随着技术的缩小，模拟神经元很难缩放，并且它们的电压净空/动态范围和电路非线性降低。鉴于此，本文首先对以28nm工艺设计的现有基于当前的电压域神经元的非线性行为进行建模，并通过神经元的非线性效果严重降低了SNN推理精度。然后，为了减轻这个问题，我们提出了一种新型的神经元，该神经元在时间域中处理传入的尖峰并极大地提高了线性性，从而提高了与现有的电压域神经元相比的推理准确性。在MNIST数据集上测试，所提出的神经元的推理错误率与理想神经元的神经元相差不到0.1％。
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.
