论文标题
使用时间域神经元的节能高准确峰值神经网络推断
Energy-Efficient High-Accuracy Spiking Neural Network Inference Using Time-Domain Neurons
论文作者
论文摘要
由于实现人工神经网络对普遍的von Neumann体系结构的局限性，最近的研究提出了基于尖峰神经网络（SNN）的神经形态系统，以降低功率和计算成本。但是，基于当前镜子或运算放大器的常规模拟电压域（I＆F）神经元电路（I＆F）神经元电路提出了严重的问题，例如非线性或高功耗，从而降低了SNN的推理准确性或能源效率。为了同时实现出色的能源效率和高精度，本文提出了低功率的高功率线性时间域I＆F神经元电路。在28nm的CMOS过程中设计和模拟的神经元在传统的基于电流磁性神经元上的MNIST推断上的错误率降低了4.3倍以上。此外，所提出的神经元电路所消耗的功率模拟为每位神经元0.230UW，比现有电压域神经元低的数量级。
Due to the limitations of realizing artificial neural networks on prevalent von Neumann architectures, recent studies have presented neuromorphic systems based on spiking neural networks (SNNs) to reduce power and computational cost. However, conventional analog voltage-domain integrate-and-fire (I&F) neuron circuits, based on either current mirrors or op-amps, pose serious issues such as nonlinearity or high power consumption, thereby degrading either inference accuracy or energy efficiency of the SNN. To achieve excellent energy efficiency and high accuracy simultaneously, this paper presents a low-power highly linear time-domain I&F neuron circuit. Designed and simulated in a 28nm CMOS process, the proposed neuron leads to more than 4.3x lower error rate on the MNIST inference over the conventional current-mirror-based neurons. In addition, the power consumed by the proposed neuron circuit is simulated to be 0.230uW per neuron, which is orders of magnitude lower than the existing voltage-domain neurons.
