布尔激活的整数卷积神经网络:BoolHash算法

Grigor Gatchev, V. Mollov
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引用次数: 2

摘要

提高卷积神经网络(CNN)的效率往往依赖于纯整数算法。使用布尔激活可以带来进一步的推理速度增益,并且可以简化cnn专用asic的设计。我们在这里提出的一种称为BoolHash的卷积算法可以将推理速度提高几倍,并允许通常需要更复杂处理的功能。使用具有16位输入权值、8位滤波器权值和1位激活的CNN模型来比较BoolHash的速度与经典加权加法器卷积算法的速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integer Convolutional Neural Networks with Boolean Activations: The BoolHash Algorithm
Improving the efficiency of convolutional neural networks (CNN) often relies on integer-only algorithms. Using boolean activations can bring further inference speed gain, and can make easier the design of CNN-specific ASICs. A convolutional algorithm called BoolHash that we propose here can additionally increase the inference speed several times, and permits functionalities that usually require more complex processing. A CNN model with 16-bit input weights, 8-bit filter weights and 1-bit activations was used to compare the speed of BoolHash to that of a classic weight-adder convolutional algorithm.
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