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

计算系统执行形成全球经济的支柱的重要应用，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。新兴的下一代应用包括基因组测序、精准医学、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，导致内存占用量不断增加。应用程序性能取决于内存系统，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）和离线处理之间的差异，使一些应用程序不切实际。能源效率低下导致更高的能源成本和计算机系统不断增长的碳足迹。内存计算（CIM）系统通过在更接近数据的地方执行计算来减少数据移动，从而显著提高速度并节省能源。然而，商业采用受到重大系统挑战的阻碍。我的研究计划的长期目标是为未来的存储系统在性能、安全性和能效方面实现突破性改进。这有利于加拿大经济，使关键的下一代应用程序的实时执行，并为学术界和工业界的未来职业培训高素质的人才。该提案解决了CIM系统面临的两个关键但大多数尚未探索的挑战：安全性和可靠性。CIM安全/隐私。内存容易受到物理、隐蔽和侧信道攻击，这些攻击可能会泄露程序的私有数据。为了避免泄漏，存储器数据被加密，并且仅当其进入可信执行域（处理器和高速缓存）时才被解密。为了保持数据完整性和防止损坏，使用消息认证码（MAC）。由于CIM计算的是未加密的数据，解密和检查完整性的开销会减少或消除CIM的好处。CIM可靠性。存储器系统中的错误率由于存储器容量缩放（更高数量的更脆弱的位单元）和破坏数据的恶意软件而增加。错误可以表现为由于可检测但不可纠正的错误或不可检测的错误而导致的失败。存储器部署纠错码（ECC）以防止故障。不幸的是，较慢的ECC（以抵消较高的错误率）会降低CIM的性能和能量增益。尽管它们对CIM的商业应用很重要，但很少有研究来解决这两个挑战。该提案针对新的软件，系统和架构机制，以填补这一关键空白，具有以下目标：（1）建模CIM系统的安全性和可靠性特征;（2）加速加密数据的计算;（3）探索和减轻对CIM系统的侧信道攻击;（4）加速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.