JSSC 2024第1期Digital Circuits

C-DNN An Energy-Efficient Complementary Deep-Neural-Network Processor With Heter

提出一种结合CNN和SNN的互补DNN处理器，实现高效能推理与训练。

85.8 TOPS/W (CIFAR-10), 79.9 TOPS/W (CIFAR-100)

深度神经网络卷积神经网络脉冲神经网络能效优化ASIC

▸结合CNN和SNN的异构核心架构，支持互补推理与训练

▸集成CNN-SNN工作负载分配器和注意力模块，优化能耗

▸采用分布式L1缓存和FDWSG技术，减少内存访问和训练操作

Abstract

In this article, we propose a complementary deep- neural-network (C-DNN) processor by combining convolutional neural network (CNN) and spiking neural network (SNN) to take advantage of them. The C-DNN processor can support both complementary inference and training with heterogeneous CNN and SNN core architecture. In addition, the C-DNN processor is the first DNN accelerator application-specific integrated circuit (ASIC) that can support CNN–SNN workload division by using their magnitude–energy t