1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AR2020★ 1 cited
ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers
Linghao Song, Fan Chen, Xuehai Qian +2
Resistive random access memory (ReRAM) is a promising technology that can perform low-cost and in-situ matrix-vector multiplication (MVM) in analog domain. Scientific computing req…
cs.LG2019
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment
Jingyang Zhang, Huanrui Yang, Fan Chen +2
Emerging resistive random-access memory (ReRAM) has recently been intensively investigated to accelerate the processing of deep neural networks (DNNs). Due to the in-situ computati…
cs.LG2019
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
Linghao Song, Fan Chen, Steven R. Young +3
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine b…