176 citations · 265 across the 10 of their papers we have counts for
10 papers
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Yiyang Zhao, Yunzhuo Liu, Bo Jiang +1
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresse…
Multi-Objective Neural Architecture Search by Learning Search Space Partitions
Yiyang Zhao, Linnan Wang, Tian Guo
Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This resu…
Carbon-Efficient Neural Architecture Search
Yiyang Zhao, Tian Guo
This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The propose…
Multi-objective Optimization by Learning Space Partitions
Yiyang Zhao, Linnan Wang, Kevin Yang +3
In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are no…
Few-shot Neural Architecture Search
Yiyang Zhao, Linnan Wang, Yuandong Tian +2
Efficient evaluation of a network architecture drawn from a large search space remains a key challenge in Neural Architecture Search (NAS). Vanilla NAS evaluates each architecture…
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai +2
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires…
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks
Linnan Wang, Wei Wu, Junyu Zhang +4
The performance and efficiency of distributed training of Deep Neural Networks highly depend on the performance of gradient averaging among all participating nodes, which is bounde…
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai +2
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires…
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Linnan Wang, Jinmian Ye, Yiyang Zhao +5
Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) pr…
Efficient Communications in Training Large Scale Neural Networks
Linnan Wang, Wei Wu, George Bosilca +2
We consider the problem of how to reduce the cost of communication that is required for the parallel training of a neural network. The state-of-the-art method, Bulk Synchronous Par…