2020/05/08 by Xiaotao Jia, Jia, Xiaotao, Jianlei Yang +10 · 2 citations
Computer Science · Mathematics · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2005.03857
accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
arxiv created 2020/05/08 · openalex publication_date 2020/05/08 · arxiv updated 2020/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bayesian method is capable of capturing real world uncertainties/incompleteness and properly addressing the over-fitting issue faced by deep neural networks. In recent years, Bayesian Neural Networks (BNNs) have drawn tremendous attentions of 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 limited power budget. In this paper, an efficient BNN inference flow is proposed to reduce the computation cost then is evaluated by means of 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 to 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 DM strategy. Finally, we implement our approach in Verilog and synthesise it with 45 nm FreePDK technology. Hardware simulation results on multi-layer BNNs demonstrate that, when compared with the traditional BNN inference method, it provides an energy consumption reduction of 73% and a 4× speedup at the expense of 14% area overhead.