8 citations · 10 across the 5 of their papers we have counts for
5 papers
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…
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.…
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…
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Christopher Wolters, Brady Taylor, Edward Hanson +3
With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data mov…
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Shiyu Li, Edward Hanson, Hai Li +1
Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy t…