The memory system continues to be a major performance and power bottleneck in nearly all computing systems. And, it is becoming increasingly more so with major application, architecture, and technology trends. Embedded applications that acquire and process real-time data, Internet and cloud applications that have to analyze large databases, and the exa-scale era HPC applications that need to crunch voluminous data sets are just a few examples of increasingly data-intensive applications that require high memory capacity, performance, and energy efficiency. Thus, the well-known memory wall problem has become even more difficult to surmount and needs a fundamental rethinking of the memory hierarchy design for future computing platforms.
The goal of this proposal is to fundamentally and holistically rethink the design of the entire memory hierarchy taking into consideration the emerging device/memory technologies and to exploit the design trade-offs at different layers of the system stack -- from devices to micro-architecture, compilers and runtime systems. The solution will cover innovations in architecting and optimizing the entire memory path consisting of the caches, on-chip networks, memory controller and main memory. The objective is to enable 100X improvement in memory capacity over the next decade, while providing 5X improvement in performance and 10X improvement in energy efficiency. The proposed research has the potential to transform the design of next-generation memory systems for the multi-core era, which is expected to be a ubiquitous part of the entire IT sector. The cross-cutting nature of this research can foster new research directions in several areas, spanning technology/energy-aware design, computer architecture, compilers, and system/application software. With the memory system forming the backbone of nearly every envisioned future application domain, the broader impact of this research can accelerate the design and deployment of future applications. This project will enable transfer of research results to industry, enhance undergraduate and graduate student training including under-represented students, and contribute to the development of new research and teaching tools.
