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Energy-aware run-time task partition and allocation in dynamic partial reconfigurable systems.

, , and . J. Syst. Archit., (2017)

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A 8-b-Precision 6T SRAM Computing-in-Memory Macro Using Segmented-Bitline Charge-Sharing Scheme for AI Edge Chips., , , , , , , , , and 15 other author(s). IEEE J. Solid State Circuits, 58 (3): 877-892 (March 2023)A Local Computing Cell and 6T SRAM-Based Computing-in-Memory Macro With 8-b MAC Operation for Edge AI Chips., , , , , , , , , and 11 other author(s). IEEE J. Solid State Circuits, 56 (9): 2817-2831 (2021)15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices., , , , , , , , , and 10 other author(s). ISSCC, page 244-246. IEEE, (2020)Energy-aware run-time task partition and allocation in dynamic partial reconfigurable systems., , and . J. Syst. Archit., (2017)A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices., , , , , , , , , and 8 other author(s). ISSCC, page 245-247. IEEE, (2021)16.3 A 28nm 384kb 6T-SRAM Computation-in-Memory Macro with 8b Precision for AI Edge Chips., , , , , , , , , and 11 other author(s). ISSCC, page 250-252. IEEE, (2021)15.5 A 28nm 64Kb 6T SRAM Computing-in-Memory Macro with 8b MAC Operation for AI Edge Chips., , , , , , , , , and 17 other author(s). ISSCC, page 246-248. IEEE, (2020)Two-Way Transpose Multibit 6T SRAM Computing-in-Memory Macro for Inference-Training AI Edge Chips., , , , , , , , , and 15 other author(s). IEEE J. Solid State Circuits, 57 (2): 609-624 (2022)15.2 A 28nm 64Kb Inference-Training Two-Way Transpose Multibit 6T SRAM Compute-in-Memory Macro for AI Edge Chips., , , , , , , , , and 13 other author(s). ISSCC, page 240-242. IEEE, (2020)A Two-way SRAM Array based Accelerator for Deep Neural Network On-chip Training., , , , , , , , , and . DAC, page 1-6. IEEE, (2020)