4 citations · 9 across the 10 of their papers we have counts for
5 papers · 1 filter
U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration d…
Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Emerging non-volatile memory (NVM)-based Computing-in-Memory (CiM) architectures show substantial promise in accelerating deep neural networks (DNNs) due to their exceptional energ…
Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise
Zheyu Yan, Yifan Qin, Wujie Wen +2
Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is promising for accelerating deep neural networks (DNNs) owing to its in-situ data processing capability and…
On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators
Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu +1
Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to strin…
NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
Yifan Qin, Zheyu Yan, Dailin Gan +5
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in…