48 citations · 49 across the 3 of their papers we have counts for
4 papers
PIM-GPT: A Hybrid Process-in-Memory Accelerator for Autoregressive Transformers
Yuting Wu, Ziyu Wang, Wei D. Lu
Decoder-only Transformer models such as GPT have demonstrated exceptional performance in text generation, by autoregressively predicting the next token. However, the efficacy of ru…
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Ziyu Wang, Yuting Wu, Yongmo Park +4
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of…
Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training
Yuting Wu, Qiwen Wang, Ziyu Wang +5
The need for deep neural network (DNN) models with higher performance and better functionality leads to the proliferation of very large models. Model training, however, requires in…
Hierarchical Architectures in Reservoir Computing Systems
John Moon, Wei D. Lu
Reservoir computing (RC) offers efficient temporal data processing with a low training cost by separating recurrent neural networks into a fixed network with recurrent connections…