1.1k citations · 2.2k across the 10 of their papers we have counts for
10 papers
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
Berkin Akin, Suyog Gupta, Yun Long +6
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high co…
Discovering Multi-Hardware Mobile Models via Architecture Search
Grace Chu, Okan Arikan, Gabriel Bender +7
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another impor…
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Yunyang Xiong, Hanxiao Liu, Suyog Gupta +7
Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In…
Accelerator-aware Neural Network Design using AutoML
Suyog Gupta, Berkin Akin
While neural network hardware accelerators provide a substantial amount of raw compute throughput, the models deployed on them must be co-designed for the underlying hardware archi…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu, Suyog Gupta
Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports…