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15.2 A 28nm 64Kb Inference-Training Two-Way Transpose Multibit 6T SRAM Compute-in-Memory Macro for AI Edge Chips.

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Circuit Design Challenges in Computing-in-Memory for AI Edge Devices., , , , , , , , , and 1 other author(s). ASICON, page 1-4. IEEE, (2019)Cross-Layer Optimizations in Solid-State Drives., , , and . IEEE Embed. Syst. Lett., 3 (4): 109-112 (2011)A Floating-Point 6T SRAM In-Memory-Compute Macro Using Hybrid-Domain Structure for Advanced AI Edge Chips., , , , , , , , , and 7 other author(s). IEEE J. Solid State Circuits, 59 (1): 196-207 (January 2024)A 2.75-to-75.9TOPS/W Computing-in-Memory NN Processor Supporting Set-Associate Block-Wise Zero Skipping and Ping-Pong CIM with Simultaneous Computation and Weight Updating., , , , , , , , , and 7 other author(s). ISSCC, page 238-240. IEEE, (2021)Empowering Local Differential Privacy: A 5718 TOPS/W Analog PUF-Based In-Memory Encryption Macro for Dynamic Edge Security., , , , , , , , , and 4 other author(s). VLSI Technology and Circuits, page 1-2. IEEE, (2024)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 22nm 832Kb Hybrid-Domain Floating-Point SRAM In-Memory-Compute Macro with 16.2-70.2TFLOPS/W for High-Accuracy AI-Edge Devices., , , , , , , , , and 7 other author(s). ISSCC, page 126-127. IEEE, (2023)A 4-Kb 1-to-8-bit Configurable 6T SRAM-Based Computation-in-Memory Unit-Macro for CNN-Based AI Edge Processors., , , , , , , , , and 8 other author(s). IEEE J. Solid State Circuits, 55 (10): 2790-2801 (2020)34.2 A 16nm 96Kb Integer/Floating-Point Dual-Mode-Gain-Cell-Computing-in-Memory Macro Achieving 73.3-163.3TOPS/W and 33.2-91.2TFLOPS/W for AI-Edge Devices., , , , , , , , , and 5 other author(s). ISSCC, page 568-570. IEEE, (2024)A 48 TOPS and 20943 TOPS/W 512kb Computation-in-SRAM Macro for Highly Reconfigurable Ternary CNN Acceleration., , , , , , , , , and . A-SSCC, page 1-3. IEEE, (2021)