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A Statistical View of Architecture D...

Deng, Zhaoxia.

A Statistical View of Architecture Design.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Statistical View of Architecture Design.
Author:	Deng, Zhaoxia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2017
Description:	145 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Notes:	Adviser: Frederic T. Chong.
Contained By:	Dissertation Abstracts International79-08B(E).
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10636241
ISBN:	9780355733969

A Statistical View of Architecture Design.
Deng, Zhaoxia.

A Statistical View of Architecture Design. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 145 p.

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--University of California, Santa Barbara, 2017.

Computer architectures are becoming more and more complicated to meet the continuously increasing demand on performance, security and sustainability from applications. Many factors exist in the design and engineering space of various components and policies in the architectures, and it is not intuitive how these factors interact with each other and how they make impacts on the architecture behaviors. Seeking for the best architectures for specific applications and requirements automatically is even more challenging. Meanwhile, the architecture design need to deal with more and more non-determinism from lower level technologies. Emerging technologies exhibit statistical properties inherently, such as the wearout phenomenon in NEMs, PCM, ReRAM, etc. Due to the manufacturing and processing variations, there also exists variability among different devices or within the same device (e.g. different cells on the same memory chip). Hence, to better understand and control the architecture behaviors, we introduce the statistical perspective of architecture design: by specifying the architectural design goals and the desired statistical properties, we guide the architecture design with these statistical properties and exploit a series of techniques to achieve these properties.

ISBN: 9780355733969Subjects--Topical Terms:

199325
Computer science.

A Statistical View of Architecture Design.
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Frederic T. Chong.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2017.
520 $a Computer architectures are becoming more and more complicated to meet the continuously increasing demand on performance, security and sustainability from applications. Many factors exist in the design and engineering space of various components and policies in the architectures, and it is not intuitive how these factors interact with each other and how they make impacts on the architecture behaviors. Seeking for the best architectures for specific applications and requirements automatically is even more challenging. Meanwhile, the architecture design need to deal with more and more non-determinism from lower level technologies. Emerging technologies exhibit statistical properties inherently, such as the wearout phenomenon in NEMs, PCM, ReRAM, etc. Due to the manufacturing and processing variations, there also exists variability among different devices or within the same device (e.g. different cells on the same memory chip). Hence, to better understand and control the architecture behaviors, we introduce the statistical perspective of architecture design: by specifying the architectural design goals and the desired statistical properties, we guide the architecture design with these statistical properties and exploit a series of techniques to achieve these properties.
520 $a In the first part of the thesis, we introduce Herniated Hash Tables. Our architectural design goal is that the hash table implementation is highly scalable in both storage efficiency and performance, while the desired statistical property is to achieve as good storage efficiency and performance as with uniform distributions given non-uniform distributions across hash buckets. Herniated Hash Tables exploit multi-level phase change memory (PCM) to in-place expand storage for each hash bucket to accommodate asymmetrically chained entries. The organization, coupled with an addressing and prefetching scheme, also improves performance significantly by creating more memory parallelism.
520 $a In the second part of the thesis, we introduce Lemonade from Lemons, harnessing device wearout to create limited-use security architectures . The architectural design goal is to create hardware security architectures that resist attacks by statistically enforcing an upper bound on hardware uses, and consequently attacks. The desired statistical property is that the system-level minimum and maximum uses can be guaranteed with high probabilities despite of device-level variability. We introduce techniques for architecturally controlling these bounds and explore the cost in area, energy and latency of using these techniques to achieve systemlevel usage targets given device-level wearout distributions.
520 $a In the third part of the thesis, we demonstrate Memory Cocktail Therapy: A General, Learning-Based Framework to Optimize Dynamic Tradeoffs in NVMs. Limited write endurance and long latencies remain the primary challenges of building practical memory systems from NVMs. Researchers have proposed a variety of architectural techniques to achieve different tradeoffs between lifetime, performance and energy efficiency; however, no individual technique can satisfy requirements for all applications and different objectives. Our architectural design goal is that NVM systems can achieve optimal tradeoffs for specific applications and objectives, and the statistical goal is that the selected NVM configuration is nearly optimal. Memory Cocktail Therapy uses machine learning techniques to model the architecture behaviors in terms of all the configurable parameters based on a small number of sample configurations. Then, it selects the optimal configuration according to user-defined objectives which leads to the desired tradeoff between performance, lifetime and energy efficiency.
590 $a School code: 0035.
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