Advancing architecture optimizations with Bespoke Analysis and Machine Learning

Abstract

With transistor scaling nearing atomic dimensions and leakage power dissipation imposing strict energy limitations, it has become increasingly difficult to improve energy efficiency in modern processors without sacrificing performance and functionality. One way to avoid this tradeoff and reduce energy without reducing performance or functionality is to take a cue from application behavior and eliminate energy in areas that will not impact application performance. This approach is especially relevant in embedded systems, which often have ultra-low power and energy requirements and typically run a single application over and over throughout their operational lifetime. In such processors, application behavior can be effectively characterized and leveraged to identify opportunities for ``free'' energy savings. We find that in addition to instruction-level sequencing, constraints imposed by program-level semantics can be used to automate processor customization and further improve energy efficiency. This dissertation describes automated techniques to identify, form, propagate, and enforce application-based constraints in gate-level simulation to reveal opportunities to optimize a processor at the design level. While this can significantly improve energy efficiency, if the goal is truly to maximize energy efficiency, it is important to consider not only design-level optimizations but also architectural optimizations. That being said, architectural optimization presents several challenges. First, the symbolic simulation tool used to characterize gate-level behavior of an application must be written anew for each new architecture. Given the expansiveness of the architectural parameter space, this is not feasible. To overcome this barrier, we developed a generic symbolic simulation tool that can handle any design, technology, or architecture, making it possible to explore application-specific architectural optimizations. However, exploring each parameter variation still requires synthesizing a new design and performing application-specific optimizations, which again becomes infeasible due to the large architecture parameter space. Given the wide usage of Machine Learning (ML) for effective design space exploration, we sought the aid of ML to efficiently explore the architectural parameter space. We built a tool that takes into account the impacts of architectural optimizations on an application and predicts the architectural parameters that result in near-optimal energy efficiency for an application. This dissertation explores the objective, training, and inference of the ML model in detail. Inspired by the ability of ML-based tools to automate architecture optimization, we also apply ML-guided architecture design and optimization for other challenging problems. Specifically, we target cache replacement, which has historically been a difficult area to improve performance. Furthermore, improvements have historically been ad hoc and highly based on designer skill and creativity. We show that ML can be used to automate the design of a policy that meets or exceeds the performance of the current state-of-art.