2 papers
cs.LG2018
Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
Haiyue Song, Chengwen Xu, Qiang Xu +4
Neural approximate computing gains enormous energy-efficiency at the cost of tolerable quality-loss. A neural approximator can map the input data to output while a classifier deter…
cs.LG2018
AXNet: ApproXimate computing using an end-to-end trainable neural network
Zhenghao Peng, Xuyang Chen, Chengwen Xu +4
Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approx…