Accelerating Next-Generation Applications Via Secure and Reliable Compute-in-Memory Systems

计算系统执行构成全球经济支柱的重要应用程序，例如机器学习、数据和图形分析、事务处理和高性能计算应用程序。新兴的下一代应用包括基因组测序、精准医疗、虚拟/增强现实、自动驾驶汽车和智能家居/城市。这些“大数据”应用程序具有快速增长的数据需求，导致内存占用不断增加。应用程序性能依赖于内存系统，并受其限制。处理器和内存之间频繁的数据移动会导致执行速度变慢和能源效率低下。较慢的执行速度可能是在线（实时）处理和离线处理之间的区别，这会使一些应用程序变得不切实际。能源效率低下导致更高的能源成本和不断增长的计算机系统碳足迹。内存计算（CIM）系统通过执行更接近数据的计算来减少数据移动，从而显著提高速度并节省能源。然而，商业应用受到重大系统挑战的阻碍。我的研究项目的长期目标是为未来的存储系统在性能、安全性和能源效率方面取得突破性进展。通过实时执行关键的下一代应用程序，并为学术界和工业界的未来职业培养高素质人才，这有利于加拿大的经济。该建议解决了CIM系统的两个关键但大多未被探索的挑战：安全性和可靠性。CIM安全/隐私。内存容易受到物理、隐蔽和侧通道攻击，这些攻击可能会泄露程序的私有数据。为了避免泄漏，内存数据是加密的，只有当它进入可信执行域（处理器和缓存）时才会解密。为了维护数据完整性和防止损坏，使用消息身份验证码（MAC）。由于CIM是在未加密的数据上进行计算的，因此解密和检查完整性的开销会减少或消除CIM的好处。CIM的可靠性。由于内存容量的增加（易受攻击的位单元数量增加）和恶意软件破坏数据，内存系统中的错误率正在增加。错误可以表现为由于可检测但无法纠正的错误或无法检测到的错误而导致的失败。内存采用纠错码（error-correcting codes， ECC）来防止故障。不幸的是，缓慢的ECC（以抵消较高的错误率）降低了CIM的性能和能源收益。尽管它们对CIM的商业采用很重要，但解决这两个挑战的研究很少。本建议以新颖的软件、系统和体系结构机制为目标，通过以下目标来填补这一关键空白：(1)为CIM系统建模安全性和可靠性特征；(2)加速加密数据的计算；(3)探索和缓解CIM系统的侧信道攻击；(4)加速对不可靠数据的可靠计算。

Computing systems execute important applications that form the backbone of the global economy, e.g., machine learning, data and graph analytics, transaction processing, and high-performance computing applications. Emerging next-generation applications include genome sequencing, precision medicine, virtual/augmented reality, autonomous vehicles, and smart homes/cities. These "Big Data" applications have fast-growing data requirements, leading to an ever-increasing memory footprint. Application performance is dependent on, and limited by, the memory system. Frequent data movements between processors and memory lead to slower execution and energy inefficiency. Slower execution could be the difference between online (real-time) and offline processing, making some applications impractical. Energy inefficiency leads to higher energy costs and a growing carbon footprint for computer systems. Compute in Memory (CIM) systems reduce data movement by performing computations closer to data, leading to significant speedups and energy savings. However, commercial adoption has been hindered by significant system challenges. My research program's long-term goal is to enable breakthrough improvements in performance, security, and energy efficiency for future memory systems. This benefits the Canadian economy by enabling real-time execution of key next-generation applications, and training highly qualified personnel for future careers in academia and industry. This proposal addresses two critical but mostly unexplored challenges for CIM systems: Security and reliability. CIM Security/Privacy. Memory is vulnerable to physical, covert and side-channel attacks that could leak programs' private data. To avoid leaks, memory data is encrypted, and is only decrypted when it enters the trusted execution domain (processors and caches). To maintain data integrity and prevent corruption, message authentication codes (MAC) are used. Since CIM computes on unencrypted data, decryption and checking integrity's overheads reduce or eliminate CIM benefits. CIM Reliability. Error rates in memory systems are increasing due to memory capacity scaling (higher number of more vulnerable bit cells), and malicious software that corrupts data. Errors can manifest as failures due to detectable but uncorrectable errors or undetectable errors. Memories deploy error-correcting codes (ECC) to protect against failures. Unfortunately, slow ECC (to offset higher error rates) reduces CIM performance and energy gains. Despite their importance for CIM's commercial adoption, little research has been done to address both challenges. This proposal targets novel software, systems and architecture mechanisms to fill this crucial gap with the following objectives: (1) Modeling security and reliability features for CIM systems; (2) Accelerating computations on encrypted data; (3) Exploring and mitigating side-channel attacks on CIM systems; (4) Accelerating reliable computations on unreliable data.