activity
20182025
most citedAn Empirical Study of Low Precision Quantization for TinyML

12 citations · 17 across the 6 of their papers we have counts for

collaborators

8 papers

cs.SD2025

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models

Chen Feng, Yicheng Lin, Shaojie Zhuo +4

Recent advances in Automatic Speech Recognition (ASR) have demonstrated remarkable accuracy and robustness in diverse audio applications, such as live transcription and voice comma…

cs.LG2025

OmniDraft: A Cross-vocabulary, Online Adaptive Drafter for On-device Speculative Decoding

Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Shaojie Zhuo +4

Speculative decoding generally dictates having a small, efficient draft model that is either pretrained or distilled offline to a particular target model series, for instance, Llam…

cs.LG2024

Stepping Forward on the Last Mile

Chen Feng, Shaojie Zhuo, Xiaopeng Zhang +3

Continuously adapting pre-trained models to local data on resource constrained edge devices is the for model deployment. However, as models increase in size and…

cs.LG2022★ 12 cited

An Empirical Study of Low Precision Quantization for TinyML

Shaojie Zhuo, Hongyu Chen, Ramchalam Kinattinkara Ramakrishnan +5

Tiny machine learning (tinyML) has emerged during the past few years aiming to deploy machine learning models to embedded AI processors with highly constrained memory and computati…

cs.CV2019★ 5 cited

Low-Power Computer Vision: Status, Challenges, Opportunities

Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…

cs.CV2019

Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution

Chen Feng, Tao Sheng, Zhiyu Liang +9

The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems wi…