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most citedMemory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

9 citations · 19 across the 15 of their papers we have counts for

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cs.AR2023

Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators

Xueying Wu, Edward Hanson, Nansu Wang +9

Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architectures have demonstrated great potential to accelerate Deep Neural Network (DNN) training/inference.…