PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren
Abstract
With the emergence of a spectrum of high-end mobile devices, many applications that formerly required desktop-level computation capability are being transferred to these devices. However, executing Deep Neural Networks (DNNs) inference is still challenging considering the high computation and storage demands, specifically, if real-time performance with high accuracy is needed. Weight pruning of DNNs is proposed, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained, accurate, but not hardware friendly; structured pruning is coarse-grained, hardware-efficient, but with higher accuracy loss.
Topics & Concepts
PruningComputer scienceMobile deviceComputationDeep neural networksInferenceArtificial neural networkArtificial intelligenceMachine learningMobile telephonyComputer engineeringReal-time computingMobile computingDeep learningKey (lock)IoT and Edge/Fog ComputingContext-Aware Activity Recognition SystemsAdvanced Memory and Neural Computing