A predictive compiler uses properties of a program to decide how to optimize it. The compiler
is trained on a collection of programs to derive a model which determines its actions in face …

Machine-learning promises to transform compilation and software engineering, yet is
frequently limited by the scope of available datasets. In particular, there is a lack of runnable …

We explore the novel application of Large Language Models to code optimization. We
present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly …

In-context learning (ICL) with pre-trained language models (PTLMs) has shown great
success in code generation. ICL does not require training. PTLMs take as the input a prompt …

Deep neural networks (DNNs) have been ubiquitously applied in many applications, and
accelerators are emerged as an enabler to support the fast and efficient inference tasks of …

Despite ceaseless efforts, extremely large and complex optimization space makes even the
state-of-the-art compilers fail in delivering the most performant setting that can fully utilize the …

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

With easier access to powerful compute resources, there is a growing trend in the field of AI
for software development to develop larger and larger language models (LLMs) to address a …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Program synthesis strives to generate a computer program as a solution to a given problem
specification, expressed with input-output examples or natural language descriptions. The …