12 citations · 13 across the 5 of their papers we have counts for
7 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…
Tensor train decompositions on recurrent networks
Alejandro Murua, Ramchalam Ramakrishnan, Xinlin Li +2
Recurrent neural networks (RNN) such as long-short-term memory (LSTM) networks are essential in a multitude of daily live tasks such as speech, language, video, and multimodal lear…
Differentiable Mask for Pruning Convolutional and Recurrent Networks
Ramchalam Kinattinkara Ramakrishnan, Eyyüb Sari, Vahid Partovi Nia
Pruning is one of the most effective model reduction techniques. Deep networks require massive computation and such models need to be compressed to bring them on edge devices. Most…