论文标题
建立新兴应用程序的高性能和安全的内存系统和体系结构
Towards a High-performance and Secure Memory System and Architecture for Emerging Applications
论文作者
论文摘要
在本文中，我们提出了一种记忆和计算协调的方法，以彻底利用基于GPU的异质系统的特征和功能，以有效地优化应用程序的性能和隐私。具体来说，1）我们提出了一种任务意识和动态内存管理机制，以优化应用程序的延迟和内存足迹，尤其是在多任务方案中。 2）我们提出了一个新型的延迟感知内存管理框架，该框架分析了应用程序特征和硬件功能，以减少应用程序的初始化延迟和响应时间。 3）我们开发了一种新的模型提取攻击，该攻击探讨了GPU统一内存系统的脆弱性，以准确窃取私有DNN模型。 4）我们提出了一个CPU/GPU共同加入机制，该机制可以在集成的CPU/GPU平台中防御定时相关攻击，以为边缘应用程序提供安全的执行环境。 该论文旨在在GPU异质平台中开发高性能和安全的内存系统和体系结构，以有效，安全地部署Emerering AI-ai-ai-ables的应用程序。
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.
