Dissertation/Thesis Abstract

Building Scalable Architectures Using Emerging Memory Technologies
by Korgaonkar, Kunal Kishore, Ph.D., University of California, San Diego, 2019, 121; 13882185
Abstract (Summary)

A confluence of trends is reshaping computing today. On one end, the massive amounts of data being generated by the proliferation of sensing and internet services are creating a demand for better computer architectures and systems. The other stream of the confluence is the nanotechnology advances that are unearthing new memory device technologies with the potential to replace (or be combined with) conventional memories.

Given these trends, this thesis examines emerging memory device technologies that provide a unique opportunity to build computer architectures with efficient and scalable data storage and processing capabilities. The associated memory architectures of these new systems promise to offer distinctive features such as intrinsic non-volatility, highly dense memory structures, extremely low-power consumption and even embedded processing capabilities. Among others, some examples of emerging memory technologies with such features are PCM, 3D Xpoint, STT-RAM and ReRAM.

A central question with the new memory architectures built with emerging memory technologies is whether or not the resultant systems are scalable. Towards answering this question, this thesis identifies that conventional memory architecture specific scaling methods may not directly apply in case of emerging memory technologies. These methods were developed mostly for SRAM and DRAM, and today, they do not provide the desired outcomes for emerging memory technologies. As a result, there exist fundamental unsolved problems concerning scalability in building memory architectures. Unfortunately, this means that even though emerging memory technologies provide distinctive features, they may be largely left untapped.

Given the scalability concerns, this thesis then advocates a scalability-first approach for building computer architectures using emerging memory technologies while being aware of the limitations and opportunities associated with them. As demonstrations of the scalability-first approach, the thesis discusses several scalability problems encountered in systems using emerging memory technologies. It also brings out potential solutions for each of these problems in the form of novel techniques and tools.

For instance, the thesis discusses the problem and a solution for scaling write order enforcement mechanisms for data persistence on large non-volatile main memory systems, followed by the problem and a potential solution for scaling write bandwidth and thereby reducing memory interference on systems with dense non-volatile memory caches. Also discussed are methods for scaling system architectures with in-memory processing capability subject to its operational complexity and other limits.

The proposed scalability-first approach points to prospects and ways for better adoption of emerging memory technologies within existing systems. The approach and the solutions also lead to likely transition paths to even more scalable and markedly different systems of the future.

Indexing (document details)
Advisor: Swanson, Steven
Commitee: Cheng, Chung-Kuan, Cosman, Pamela, Gupta, Rajesh, Kastner, Ryan
School: University of California, San Diego
Department: Computer Science and Engineering
School Location: United States -- California
Source: DAI-B 81/3(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer Engineering, Computer science
Keywords: Data-centric systems, Nanotechnologies, Non-Volatile Memories, Non-Volatile Memory Architectures, Non-Volatile Processors, Processing in Memory
Publication Number: 13882185
ISBN: 9781088326817
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