一句话：提出通用神经网络编译器，将无约束训练网络转换为满足神经形态硬件限制的等效网络。
面向神经形态硬件开发神经网络常受硬件限制，如信号与参数精度有限、计算规模受限、非线性函数种类少。本文提出一种通用方法：通过编译器将已有训练完成且无硬件约束的神经网络，自动转换为满足目标硬件约束的等效网络，从而解耦应用与硬件。作者设计多项变换技术，使方案适应不同神经网络芯片并保证在严格约束下可靠，工具同时支持脉冲神经网络（SNN）与传统人工神经网络（ANN）。在流片实现的神经形态芯片与存内计算（PIM）设计上验证。结果表明推理误差不显著，变换时间远短于重训练；参数敏感性评估揭示了网络误差与资源利用率的权衡，可为软硬件协同设计优化提供指导。
Different from developing neural networks (NNs) for general-purpose processors, the development for NN chips usually faces with some hardware-specific restrictions, such as limited precision of network signals and parameters, constrained computation scale, and limited types of non-linear functions. This paper proposes a general methodology to address the challenges. We decouple the NN applications from the target hardware by introducing a compiler that can transform an existing trained, unrestricted NN into an equivalent network that meets the given hardware's constraints. We propose multiple techniques to make the transformation adaptable to different kinds of NN chips, and reliable for restrict hardware constraints. We have built such a software tool that supports both spiking neural networks (SNNs) and traditional artificial neural networks (ANNs). We have demonstrated its effectiveness with a fabricated neuromorphic chip and a processing-in-memory (PIM) design. Tests show that the inference error caused by this solution is insignificant and the transformation time is much shorter than the retraining time. Also, we have studied the parameter-sensitivity evaluations to explore the tradeoffs between network error and resource utilization for different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
DOI 原文 ·
@inproceedings{paperbot4240,
title = {Bridge the Gap between Neural Networks and Neuromorphic Hardware with a Neural Network Compiler},
author = {Yu Ji and Youhui Zhang and Wenguang Chen and Yuan Xie},
booktitle = {ASPLOS},
year = {2018},
doi = {10.1145/3173162.3173205}
}