摘要
High computational energy-efficiency and rapid real-timeresponse are the major concerns for applications of artificial intelligencein low-power mobile and Internet of Things deviceswith limited storage capacity. Due to the outstanding superiorityof less memory requirement, low computation overheadand negligible accuracy degradation, deep neural networkswith binary/ternary weights (BTNNs) have been widely adoptedto replace traditional full-precision neural networks.
