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 …

Precise hardware performance models play a crucial role in code optimizations. They can
assist compilers in making heuristic decisions or aid autotuners in identifying the optimal …

Memory buffer allocation for on-chip memories is a major challenge in modern machine
learning systems that target ML accelerators. In interactive systems such as mobile phones …

Neural architecture search (NAS) has become an increasingly important tool within the deep
learning community in recent years, yielding many practical advancements in the design of …

We introduce a novel paradigm in compiler optimization powered by Large Language
Models with compiler feedback to optimize the code size of LLVM assembly. The model …

Deep learning models rely on highly optimized tensor libraries for efficient inference on
heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors …

Users may now choose from a vast range of compiler optimizations. These optimizations
interact in a variety of sophisticated ways with one another and with the source code. The …

Kernel scheduling is the problem of finding the most efficient implementation for a
computational kernel. Identifying this implementation involves experimenting with the …

Resource scheduling and allocation is a critical component of many high impact systems
ranging from congestion control to cloud computing. Finding more optimal solutions to these …

For the past 25 years, we have witnessed an extensive application of Machine Learning to
the Compiler space; the selection and the phase-ordering problem. However, limited works …