1 citations · 3 across the 4 of their papers we have counts for
4 papers
Deep photonic reservoir computing recurrent network
Cheng Wang
Deep neural networks usually process information through multiple hidden layers. However, most hardware reservoir computing recurrent networks only have one hidden reservoir layer,…
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators
Sourjya Roy, Cheng Wang, Anand Raghunathan
Progress in artificial intelligence and machine learning over the past decade has been driven by the ability to train larger deep neural networks (DNNs), leading to a compute deman…
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Wilfried Haensch, Anand Raghunathan, Kaushik Roy +4
Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of tod…
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing
Bing Han, Cheng Wang, Kaushik Roy
Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/v…