2 citations · 5 across the 5 of their papers we have counts for
5 papers
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration
Richard Petri, Grace Li Zhang, Yiran Chen +2
Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the larg…
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…
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance
Huanrui Yang, Xiaoxuan Yang, Neil Zhenqiang Gong +1
With the recent demand of deploying neural network models on mobile and edge devices, it is desired to improve the model's generalizability on unseen testing data, as well as enhan…
Harnessing Optoelectronic Noises in a Photonic Generative Network
Changming Wu, Xiaoxuan Yang, Heshan Yu +4
Integrated optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity. The…