Search Results for author: Kevin Hsieh

Found 9 papers, 1 papers with code

FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations

no code implementations2 Feb 2022 Jinhyun So, Kevin Hsieh, Behnaz Arzani, Shadi Noghabi, Salman Avestimehr, Ranveer Chandra

To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites.

Federated Learning

Interpret-able feedback for AutoML systems

no code implementations22 Feb 2021 Behnaz Arzani, Kevin Hsieh, Haoxian Chen

Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts.

Active Learning AutoML +1

Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers

no code implementations19 Dec 2020 Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan, Junchen Jiang, Nikolaos Karianakis, Yuanchao Shu, Kevin Hsieh, Victor Bahl, Ion Stoica

Compressed models that are deployed on the edge servers for inference suffer from data drift, where the live video data diverges from the training data.

Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data

no code implementations18 Oct 2019 Kevin Hsieh

The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost.

BIG-bench Machine Learning

The Non-IID Data Quagmire of Decentralized Machine Learning

1 code implementation ICML 2020 Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons

Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing significant accuracy loss across many ML applications, DNN models, training datasets, and decentralized learning algorithms; (ii) the problem is particularly challenging for DNN models with batch normalization; and (iii) the degree of data skew is a key determinant of the difficulty of the problem.

BIG-bench Machine Learning

Enabling the Adoption of Processing-in-Memory: Challenges, Mechanisms, Future Research Directions

no code implementations1 Feb 2018 Saugata Ghose, Kevin Hsieh, Amirali Boroumand, Rachata Ausavarungnirun, Onur Mutlu

This requires efficient mechanisms that can provide logic in DRAM with access to CPU structures without having to communicate frequently with the CPU.

Hardware Architecture

Focus: Querying Large Video Datasets with Low Latency and Low Cost

no code implementations10 Jan 2018 Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, Onur Mutlu

Focus handles the lower accuracy of the cheap CNNs by judiciously leveraging expensive CNNs at query-time.

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

no code implementations10 Nov 2017 Amr Alanwar, Henrique Ferraz, Kevin Hsieh, Rohit Thazhath, Paul Martin, Joao Hespanha, Mani Srivastava

Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion.

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