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Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges.

, , , , , , , , and . Proc. IEEE, 108 (12): 2276-2310 (2020)

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In-Memory Computing in Emerging Memory Technologies for Machine Learning: An Overview., , , , and . DAC, page 1-6. IEEE, (2020)PIM-DRAM: Accelerating Machine Learning Workloads Using Processing in Commodity DRAM., , and . IEEE J. Emerg. Sel. Topics Circuits Syst., 11 (4): 701-710 (2021)Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges., , , , , , , , and . Proc. IEEE, 108 (12): 2276-2310 (2020)A 65 nm 1.4-6.7 TOPS/W Adaptive-SNR Sparsity-Aware CIM Core with Load Balancing Support for DL workloads., , , , , , and . CICC, page 1-2. IEEE, (2023)IMPULSE: A 65nm Digital Compute-in-Memory Macro with Fused Weights and Membrane Potential for Spike-based Sequential Learning Tasks., , , , , and . CoRR, (2021)Circuits and Architectures for In-Memory Computing-Based Machine Learning Accelerators., , , , and . IEEE Micro, 40 (6): 8-22 (2020)Design Tools for Resistive Crossbar based Machine Learning Accelerators., , , , , , and . AICAS, page 1-4. IEEE, (2021)IMAC: In-Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array., , , , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 67-I (8): 2521-2531 (2020)RAMANN: in-SRAM differentiable memory computations for memory-augmented neural networks., , and . ISLPED, page 61-66. ACM, (2020)Digital and Analog-Mixed-Signal In-Memory Processing in CMOS SRAM., , , , and . ACM Great Lakes Symposium on VLSI, page 371. ACM, (2019)