1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2021
RCT: Resource Constrained Training for Edge AI
Tian Huang, Tao Luo, Ming Yan +2
Neural networks training on edge terminals is essential for edge AI computing, which needs to be adaptive to evolving environment. Quantised models can efficiently run on edge devi…
cs.LG2021★ 1 cited
QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates
Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan +3
An increasingly popular method for solving a constrained combinatorial optimisation problem is to first convert it into a quadratic unconstrained binary optimisation (QUBO) problem…
cs.LG2020
Adaptive Precision Training for Resource Constrained Devices
Tian Huang, Tao Luo, Joey Tianyi Zhou
Learn in-situ is a growing trend for Edge AI. Training deep neural network (DNN) on edge devices is challenging because both energy and memory are constrained. Low precision traini…