no code implementations • COLING 2022 • Chonghan Lee, Md Fahim Faysal Khan, Rita Brugarolas Brufau, Ke Ding, Vijaykrishnan Narayanan
While pre-trained language models like BERT have achieved impressive results on various natural language processing tasks, deploying them on resource-restricted devices is challenging due to their intensive computational cost and memory footprint.
no code implementations • 25 Mar 2022 • Cyan Subhra Mishra, Jack Sampson, Mahmut Taylan Kandemir, Vijaykrishnan Narayanan
To address these challenges, we propose \emph{Seeker}, a novel approach to efficiently execute DNN inferences for Human Activity Recognition (HAR) tasks, using both an EH-WSN and a host mobile device.
no code implementations • 16 Sep 2021 • Anup Sarma, Sonali Singh, Huaipan Jiang, Ashutosh Pattnaik, Asit K Mishra, Vijaykrishnan Narayanan, Mahmut T Kandemir, Chita R Das
By exploiting sparsity in both the forward and backward passes, speedup improvements range from 1. 68$\times$ to 3. 30$\times$ over the sparsity-agnostic baseline execution.
1 code implementation • 15 Jul 2021 • Feng Shi, Chonghan Lee, Mohammad Khairul Bashar, Nikhil Shukla, Song-Chun Zhu, Vijaykrishnan Narayanan
Our model has a scale-free structure which could process varying size of instances.
1 code implementation • 15 Jul 2021 • Feng Shi, Chonghan Lee, Liang Qiu, Yizhou Zhao, Tianyi Shen, Shivran Muralidhar, Tian Han, Song-Chun Zhu, Vijaykrishnan Narayanan
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years.
no code implementations • 8 Feb 2021 • Hanlin Lu, Ting He, Shiqiang Wang, Changchang Liu, Mehrdad Mahdavi, Vijaykrishnan Narayanan, Kevin S. Chan, Stephen Pasteris
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers.
no code implementations • 11 Apr 2019 • Hanlin Lu, Ming-Ju Li, Ting He, Shiqiang Wang, Vijaykrishnan Narayanan, Kevin S. Chan
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data.