一种嵌入式轻量化卷积神经网络计算加速方法

Embedded Lightweight Convolutional Neural Network Computing Acceleration Method

针对传统ARM处理器算力低、不适用于实时性需求比较高的应用场景的问题,本文提出了一种基于ARM处理器的单指令多数据(Single Instruction Multiple Data,SIMD)指令集的轻量化卷积神经网络计算加速方法,并将该方法用于处理脑电信号(Electroencephalogram,EEG)来进行手术过程中麻醉深度监测.通过可学习步长量化的方法得到轻量化卷积神经网络,减少浮点数的运算量,极大地提高了网络速度.采用基于ARM处理器SIMD指令集的卷积加速器,各卷积层分别可加速几十倍、几百倍,甚至一万多倍.在Ultra 96-V2开发板上用ARM处理器实现整个网络的运算,在昆士兰大学生命体征公开数据集上的测试结果表明,仅需39.64ms就可以处理时间跨度为1s的EEG单通道信号,速度提高到原来的10.5倍,且功耗仅为0.1J,在提升速度的同时基本保持网络预测的准确率,能够很好地预测出麻醉深度.

A lightweight convolutional neural network computing acceleration method based on the ARM processor SIMD(Single Instruction Multiple Data)instruction set is proposed to solve the problem that traditional ARM processors have low computing power and are not suitable for application scenarios with high real-time requirements.And this method is used to process EEG(Electroencephalogram)signals to monitor the depth of anesthesia during surgery.The lightweight convolutional neural network is obtained by the method of learned step quantization,which reduces the amount of floating-point calculations and greatly improves the network speed.Using the convolution accelerator based on the SIMD instruction set of the ARM processor,each convolutional layer can be accelerated by dozens of times,hundreds of times,or even more than 10000 times respectively.Use ARM processor on Ultra 96-V2 board to implement the entire network,the test results on the University of Queensland Vital Signs Public Dataset show that it only takes 39.64ms to process EEG single-channel signals with a time span of one second,speeding up to 10.5 times,and the power consumption is only 0.1J,maintaining the accuracy while increasing the speed,predicting the depth of anesthesia well.