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09/14/2022
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Automatic Speech Recognition models require large amount of speech data ...
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05/06/2022
Online Model Compression for Federated Learning with Large Models
This paper addresses the challenges of training large neural network mod...
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10/11/2021
Partial Variable Training for Efficient On-Device Federated Learning
This paper aims to address the major challenges of Federated Learning (F...
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10/07/2021
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Federated learning can be used to train machine learning models on the e...
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03/31/2021
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization
Neural architecture search (NAS) typically consists of three main steps:...
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12/18/2019
Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators
This paper describes various design considerations for deep neural netwo...
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10/15/2019
SegSort: Segmentation by Discriminative Sorting of Segments
Almost all existing deep learning approaches for semantic segmentation t...
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03/08/2019
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
Depth sensing is a critical function for robotic tasks such as localizat...
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02/13/2019
DeeperLab: Single-Shot Image Parser
We present a single-shot, bottom-up approach for whole image parsing. Wh...
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04/09/2018
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
This work proposes an automated algorithm, called NetAdapt, that adapts ...
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11/18/2017
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
We present MorphNet, an approach to automate the design of neural networ...
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03/27/2017
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Deep neural networks (DNNs) are currently widely used for many artificia...
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11/16/2016