85 citations · 147 across the 7 of their papers we have counts for
9 papers
Low-Rank+Sparse Tensor Compression for Neural Networks
Cole Hawkins, Haichuan Yang, Meng Li +2
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices. Tensor…
Improving Efficiency in Neural Network Accelerator Using Operands Hamming Distance optimization
Meng Li, Yilei Li, Pierce Chuang +2
Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric. The data-path energy, including the computation ener…
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks
Lei Yang, Zheyu Yan, Meng Li +6
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs).…
Heterogeneous Dataflow Accelerators for Multi-DNN Workloads
Hyoukjun Kwon, Liangzhen Lai, Michael Pellauer +3
Emerging AI-enabled applications such as augmented/virtual reality (AR/VR) leverage multiple deep neural network (DNN) models for sub-tasks such as object detection, hand tracking,…
Rethinking Machine Learning Development and Deployment for Edge Devices
Liangzhen Lai, Naveen Suda
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As…
Not All Ops Are Created Equal!
Liangzhen Lai, Naveen Suda, Vikas Chandra
Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices. In this work, we point out that typical design metrics for gau…