  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.
