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6.7ms on Mobile with over 78% ImageNet Accuracy: Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration., , , , , , , , , and 6 other author(s). CoRR, (2020)Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device., , , , , , , , , and 1 other author(s). CoRR, (2020)Reversible Column Networks., , , , , , and . ICLR, OpenReview.net, (2023)Real-Time Mobile Acceleration of DNNs: From Computer Vision to Medical Applications., , , , , , , , and . ASP-DAC, page 581-586. ACM, (2021)Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search., , , , , , and . CoRR, (2021)High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost., , , , , , , , and . CoRR, (2024)Eyes Closed Elevates Brain Intrinsic Activity of Sensory Dominance Networks: A Classifier Discrimination Analysis., , , , , , , , , and . Brain Connect., 9 (2): 221-230 (2019)TinyADC: Peripheral Circuit-aware Weight Pruning Framework for Mixed-signal DNN Accelerators., , , , , , , , , and 3 other author(s). DATE, page 926-931. IEEE, (2021)A Compression-Compilation Co-Design Framework Towards Real-Time Object Detection on Mobile Devices., , , , , , , and . AAAI, page 15997-16000. AAAI Press, (2021)Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles., , , , , , , , and . DAC, page 835-840. IEEE, (2021)