Energy-Efficient Convolutional Neural Networks with Deterministic Bit-Stream Processing
Energy-Efficient Convolutional Neural Networks with Deterministic Bit-Stream Processing
复制标题
具有确定性比特流处理的节能卷积神经网络
DOI：
10.23919/date.2019.8714937
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
K. Bazargan
中科院分区：
文献类型：
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作者：
S. R. Faraji;M. Hassan Najafi;Bingzhe Li;David J. Lilja;K. Bazargan
随机计算（SC）已被用于低成本、低功耗的神经网络实现。与传统的定点设计相比，处理随机比特流固有的不准确性和长延迟使得先前基于sc的实现效率低下。随机或伪随机比特流通常需要处理很长时间才能产生可接受的结果。这种长延迟导致的能耗明显高于二进制设计。低差异序列最近被用于随机结构的快速收敛确定性计算。在这项工作中，我们提出了一种基于低差异确定性比特流的低成本，低延迟和节能的卷积神经网络实现。实验结果表明，与之前基于随机比特流的实现和优化的定点设计相比，能耗显著降低，且没有质量下降。
Stochastic computing (SC) has been used for lowcost and low power implementation of neural networks. Inherent inaccuracy and long latency of processing random bit-streams have made prior SC-based implementations inefficient compared to conventional fixed-point designs. Random or pseudo-random bitstreams often need to be processed for a very long time to produce acceptable results. This long latency leads to a significantly higher energy consumption than binary design counterparts. Low-discrepancy sequences have been recently used for fast-converging deterministic computation with stochastic constructs. In this work, we propose a low-cost, low-latency, and energy-efficient implementation of convolutional neural networks based on low-discrepancy deterministic bit-streams. Experimental results show a significant reduction in the energy consumption compared to previous random bitstream-based implementations and to the optimized fixed-point design with no quality degradation.