论文标题
实时对象分类的强力二进制二元尖峰神经网络体系结构
A Power-Efficient Binary-Weight Spiking Neural Network Architecture for Real-Time Object Classification
论文作者
论文摘要
神经网络硬件被认为是未来边缘设备的重要组成部分。在本文中，我们提出了一个二进制尖峰神经网络（BW-SNN）硬件体系结构，用于在边缘平台上进行低功率实时对象分类。该设计将在片上存储完整的神经网络，因此不需要芯片外带宽。提出的收缩期阵列可最大程度地利用典型卷积层的数据。 90nm CMOS实现了5层卷积BW-SNN硬件。与最先进的设计相比，每个分类的面积成本和能源分别降低了7 $ \ times $和23 $ \ times $，同时在MNIST基准测试中的准确性也更高。这也是支持高级CNN体系结构的开创性SNN硬件体系结构。
Neural network hardware is considered an essential part of future edge devices. In this paper, we propose a binary-weight spiking neural network (BW-SNN) hardware architecture for low-power real-time object classification on edge platforms. This design stores a full neural network on-chip, and hence requires no off-chip bandwidth. The proposed systolic array maximizes data reuse for a typical convolutional layer. A 5-layer convolutional BW-SNN hardware is implemented in 90nm CMOS. Compared with state-of-the-art designs, the area cost and energy per classification are reduced by 7$\times$ and 23$\times$, respectively, while also achieving a higher accuracy on the MNIST benchmark. This is also a pioneering SNN hardware architecture that supports advanced CNN architectures.
