87 citations · 119 across the 9 of their papers we have counts for
4 papers · 1 filter
BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge +4
Along with the progress of AI democratization, machine learning (ML) has been successfully applied to edge applications, such as smart phones and automated driving. Nowadays, more…
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
Panjie Qi, Edwin Hsing-Mean Sha, Qingfeng Zhuge +5
State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be accommodated on resource constrained embedded devices. Moreover, with the development of te…
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
Yuhong Song, Weiwen Jiang, Bingbing Li +6
A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models…
Hardware/Software Co-Exploration of Neural Architectures
Weiwen Jiang, Lei Yang, Edwin Sha +5
We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed h…