4 papers
FusionCIM: Accelerating LLM Inference with Fusion-Driven Computing-in-Memory Architecture
Zihao Xuan, Jia Chen, Yewen Li +4
In this paper, we propose FusionCIM, an operator-fusion-driven compute-in-memory (CIM) accelerator architecture for efficient and scalable LLM inference, with three key innovations…
Towards Secure and Efficient DNN Accelerators via Hardware-Software Co-Design
Wei Xuan, Zihao Xuan, Rongliang Fu +8
The rapid deployment of deep neural network (DNN) accelerators in safety-critical domains such as autonomous vehicles, healthcare systems, and financial infrastructure necessitates…
SeDA: Secure and Efficient DNN Accelerators with Hardware/Software Synergy
Wei Xuan, Zhongrui Wang, Lang Feng +6
Ensuring the confidentiality and integrity of DNN accelerators is paramount across various scenarios spanning autonomous driving, healthcare, and finance. However, current security…
DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams
Wei Xuan, Yan Liang, Huawei Cao +3
Triangle counting is a fundamental problem in graph mining, essential for analyzing graph streams with arbitrary edge orders. However, exact counting becomes impractical due to the…