4 papers
Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators
Haishan Zhu, Domi Yan, Michael Levesque-Dion +40
The rapid growth in machine learning workloads has fueled the proliferation of custom accelerator architectures. Designed from the ground up, these accelerators often expose progra…
Agentic Operator Generation for ML ASICs
Alec M. Hammond, Aram Markosyan, Aman Dontula +15
We present TritorX, an agentic AI system designed to generate functionally correct Triton PyTorch ATen kernels at scale for emerging accelerator platforms. TritorX integrates open-…
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
Glow: Graph Lowering Compiler Techniques for Neural Networks
Nadav Rotem, Jordan Fix, Saleem Abdulrasool +15
This paper presents the design of Glow, a machine learning compiler for heterogeneous hardware. It is a pragmatic approach to compilation that enables the generation of highly opti…