76 citations · 100 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
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…
2018 Low-Power Image Recognition Challenge
Sergei Alyamkin, Matthew Ardi, Achille Brighton +38
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technolog…
A Quantization-Friendly Separable Convolution for MobileNets
Tao Sheng, Chen Feng, Shaojie Zhuo +3
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as netwo…