1 citations · 1 across the 1 of their papers we have counts for
4 papers
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
Fangxin Liu, Wenbo Zhao, Yilong Zhao +6
Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for deep neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing abilities…
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…
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…
Approximate Random Dropout
Zhuoran Song, Ru Wang, Dongyu Ru +5
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the…