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A 2.5D Integrated Voltage Regulator Using Coupled-Magnetic-Core Inductors on Silicon Interposer., , , , , , , , , and 5 other author(s). J. Solid-State Circuits, 48 (1): 244-254 (2013)Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks., , , , , , , , and . NeurIPS, page 4901-4910. (2019)Innovate Practices on CyberSecurity of Hardware Semiconductor Devices., , , , , , , , , and 4 other author(s). VTS, page 1. IEEE, (2019)A 7-nm Four-Core Mixed-Precision AI Chip With 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling., , , , , , , , , and 34 other author(s). IEEE J. Solid State Circuits, 57 (1): 182-197 (2022)Efficient AI System Design With Cross-Layer Approximate Computing., , , , , , , , , and 30 other author(s). Proc. IEEE, 108 (12): 2232-2250 (2020)A 2.5D integrated voltage regulator using coupled-magnetic-core inductors on silicon interposer delivering 10.8A/mm2., , , , , , , , , and 5 other author(s). ISSCC, page 400-402. IEEE, (2012)An 82%-efficient multiphase voltage-regulator 3D interposer with on-chip magnetic inductors., , , , , , , , , and 1 other author(s). VLSIC, page 192-. IEEE, (2015)A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference., , , , , , , , , and 21 other author(s). VLSI Circuits, page 35-36. IEEE, (2018)Across the Stack Opportunities for Deep Learning Acceleration., , , , , , , , , and 21 other author(s). ISLPED, page 35:1-35:2. ACM, (2018)Training Deep Neural Networks with 8-bit Floating Point Numbers., , , , and . NeurIPS, page 7686-7695. (2018)