2022/05/09 by Zhendong Wang, Yang Hu, Wang, Zhendong +1
Computer Science · #Advanced Data Storage Technologies #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Parallel #Parallel Computing and Optimization Techniques #Security and Verification in Computing #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2205.04002
openalex publication_date 2022/05/09 · openalex created_date 2022/05/22 · openalex updated_date 2026/07/28
In this dissertation, we propose a memory and computing coordinated methodology to thoroughly exploit the characteristics and capabilities of the GPU-based heterogeneous system to effectively optimize applications' performance and privacy. Specifically, 1) we propose a task-aware and dynamic memory management mechanism to co-optimize applications' latency and memory footprint, especially in multitasking scenarios. 2) We propose a novel latency-aware memory management framework that analyzes the application characteristics and hardware features to reduce applications' initialization latency and response time. 3) We develop a new model extraction attack that explores the vulnerability of the GPU unified memory system to accurately steal private DNN models. 4) We propose a CPU/GPU Co-Encryption mechanism that can defend against a timing-correlation attack in an integrated CPU/GPU platform to provide a secure execution environment for the edge applications. This dissertation aims at developing a high-performance and secure memory system and architecture in GPU heterogeneous platforms to deploy emerging AI-enabled applications efficiently and safely.