Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

We survey some recent research directions within the field of approximate dynamic
programming, with a particular emphasis on rollout algorithms and model predictive control …

Temporal difference methods are theoretically grounded and empirically effective methods
for addressing reinforcement learning problems. In most real-world reinforcement learning …

We explore approximate policy iteration, replacing the usual costfunction learning step with
a learning step in policy space. We give policy-language biases that enable solution of very …

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely
studied and explored in academia. However, the adoption of ML in general-purpose …

Modern microprocessors are equipped with single instruction multiple data (SIMD) or vector
instruction sets which allow compilers to exploit fine-grained data level parallelism. To …

This chapter discusses rollout algorithms, a sequential approach to optimization problems,
whereby the optimization variables are optimized one after the other. A rollout algorithm …

The ability to create and to use abstractions in complex environments, that is, to
systematically ignore irrelevant details, is a key reason that humans are effective problem …

Instruction scheduling is a compiler optimization that can improve program speed,
sometimes by 10% or more, but it can also be expensive. Furthermore, time spent optimizing …