8 citations · 10 across the 5 of their papers we have counts for
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cs.AR2025
Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-aware Cache Compression
Feng Cheng, Cong Guo, Chiyue Wei +7
Large language models (LLMs) have demonstrated transformative capabilities across diverse artificial intelligence applications, yet their deployment is hindered by substantial memo…
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.…
cs.AR2023★ 2 cited
Analog, In-memory Compute Architectures for Artificial Intelligence
Patrick Bowen, Guy Regev, Nir Regev +3
This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to singl…