UC Santa Barbara

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.

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.

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.

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.