Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization
Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization
复制标题
通过特征分解和记忆有效减少贝叶斯神经网络的计算量
DOI：
10.1109/tnnls.2020.2987760
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发表时间：
2021-04-01
影响因子：
10.4
通讯作者：
Zhao, Weisheng
中科院分区：
文献类型：
--
作者：
Jia, Xiaotao;Yang, Jianlei;Zhao, Weisheng
贝叶斯方法能够捕捉真实世界的不确定性/不完整性，并适当解决深度神经网络面临的过拟合问题。近年来，贝叶斯神经网络（BNN）引起了人工智能（AI）研究人员的极大关注，并在许多应用中取得了成功。然而，所需的高计算复杂度使得BNN难以部署在具有有限功率预算的计算系统中。在这篇文章中，提出了一个有效的BNN推理流程，以减少计算成本，然后使用软件和硬件实现进行评估。利用特征分解和记忆策略对BNN的推理流程进行了改革，使其简化.理论分析和软件验证表明，与传统方法相比，该方法可以减少一半的计算量。随后，为了解决硬件资源的限制，进一步部署了一个内存友好的计算框架，以减少由DM策略引入的内存开销。最后，我们在Verilog中实现了我们的方法，并使用45 nm FreePDK技术对其进行了综合。在多层BNN上的硬件仿真结果表明，与传统的BNN推理方法相比，该方法的能耗降低了73%，速度提高了4倍，而面积开销仅为14%.
The Bayesian method is capable of capturing real-world uncertainties/incompleteness and properly addressing the overfitting issue faced by deep neural networks. In recent years, Bayesian neural networks (BNNs) have drawn tremendous attention to artificial intelligence (AI) researchers and proved to be successful in many applications. However, the required high computation complexity makes BNNs difficult to be deployed in computing systems with a limited power budget. In this article, an efficient BNN inference flow is proposed to reduce the computation cost and then is evaluated using both software and hardware implementations. A feature decomposition and memorization (DM) strategy is utilized to reform the BNN inference flow in a reduced manner. About half of the computations could be eliminated compared with the traditional approach that has been proved by theoretical analysis and software validations. Subsequently, in order to resolve the hardware resource limitations, a memory-friendly computing framework is further deployed to reduce the memory overhead introduced by the DM strategy. Finally, we implement our approach in Verilog and synthesize it with a 45-nm FreePDK technology. Hardware simulation results on multilayer BNNs demonstrate that, when compared with the traditional BNN inference method, it provides an energy consumption reduction of 73% and a $4\times $ speedup at the expense of 14% area overhead.