Browsing by Subject "Cache replacement"

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Machine learning for prediction problems in computer architecture
(2020-12) Shi, Zhan; Lin, Yun Calvin; Fussell, Don; Liu, Qiang; Hashemi, Milad
The solutions to many problems in computer architecture involve predictions, which are often based on heuristics. Given the success of machine learning in solving prediction problems, it is natural to wonder if machine learning can better solve architectural prediction problems. Unfortunately, despite vastly outperforming traditional heuristics in various fields, machine learning has seen limited impact on prediction problems in computer architecture. The main challenge is that each architectural prediction problem exhibits unique constraints that prevent off-the-shelf machine learning algorithms from being more effective than heuristics. For example, hardware prediction problems, such as branch prediction and cache replacement, impose severe latency and area constraints that make multi-layer neural networks largely infeasible. In this thesis, we propose machine learning solutions to three important problems in computer architecture, namely cache replacement, data prefetching, and the automatic design of neural network accelerators. In our solutions, we focus on not only the design of learning algorithms, but also the use of learning algorithms under the unique constraints of each problem. In particular, to deal with the extremely tight area and latency constraints of replacement policies and data prefetchers, we propose to first design powerful yet impractical neural network models, from which we derive important insights that can be used to design practical predictors. To deal with the highly constrained search space in the automated design of neural network accelerators, we propose a new constrained Bayesian optimization framework to effectively explore the search space where over 90\% of designs are infeasible.