论文标题
一个高效的软件硬件设计框架，用于尖峰神经网络系统
An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems
论文作者
论文摘要
尖峰神经网络（SNN）是模仿大脑自然行为的第三代神经网络（NN）。通过基于二进制输入/输出的处理，SNN提供了较低的复杂性，较高的密度和较低的功耗。这项工作为开发硬件中的SNN系统提供了有效的软件硬件设计框架。此外，基于数据包切换通信方法提出了低成本神经突触核心的设计。评估结果表明，尺寸为784：1200：1200：10的ANN到SNN转换方法的MNIST精度为99％，而无监督的STDP Archives 89％，尺寸为784：400，带有经常性连接。 ASIC 45nm技术还实施了256-神经元和65K突触的设计，面积为0.205 $ m m m^2 $。
Spiking Neural Network (SNN) is the third generation of Neural Network (NN) mimicking the natural behavior of the brain. By processing based on binary input/output, SNNs offer lower complexity, higher density and lower power consumption. This work presents an efficient software-hardware design framework for developing SNN systems in hardware. In addition, a design of low-cost neurosynaptic core is presented based on packet-switching communication approach. The evaluation results show that the ANN to SNN conversion method with the size 784:1200:1200:10 performs 99% accuracy for MNIST while the unsupervised STDP archives 89% with the size 784:400 with recurrent connections. The design of 256-neurons and 65k synapses is also implemented in ASIC 45nm technology with an area cost of 0.205 $m m^2$.
