24 citations · 25 across the 5 of their papers we have counts for
5 papers
DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs
Myat Thu Linn Aung, Daniel Gerlinghoff, Chuping Qu +5
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving gr…
Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases
Tian Huang, Jun Xu, Tao Luo +3
Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantu…
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…
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…
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…