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.
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