23 citations · 28 across the 6 of their papers we have counts for
6 papers
ReaLPrune: ReRAM Crossbar-aware Lottery Ticket Pruned CNNs
Biresh Kumar Joardar, Janardhan Rao Doppa, Hai Li +2
Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessib…
Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
Xiaoxuan Yang, Syrine Belakaria, Biresh Kumar Joardar +5
Resistive random-access memory (ReRAM) is a promising technology for designing hardware accelerators for deep neural network (DNN) inferencing. However, stochastic noise in ReRAM c…
Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning
Haowen Fang, Brady Taylor, Ziru Li +3
Neuromorphic computing and spiking neural networks (SNN) mimic the behavior of biological systems and have drawn interest for their potential to perform cognitive tasks with high e…
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Huanrui Yang, Lin Duan, Yiran Chen +1
Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated.…
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…
RED: A ReRAM-based Deconvolution Accelerator
Zichen Fan, Ziru Li, Bing Li +3
Deconvolution has been widespread in neural networks. For example, it is essential for performing unsupervised learning in generative adversarial networks or constructing fully con…