51 citations · 116 across the 5 of their papers we have counts for
5 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…
RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network
Sangmin Yoo, Eric Yeu-Jer Lee, Ziyu Wang +2
Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the event data requires either using expen…
Side-channel attack analysis on in-memory computing architectures
Ziyu Wang, Fan-hsuan Meng, Yongmo Park +2
In-memory computing (IMC) systems have great potential for accelerating data-intensive tasks such as deep neural networks (DNNs). As DNN models are generally highly proprietary, th…