Glow: A community-driven approach to AI infrastructure

This post announces the open-sourcing of Glow, a community-driven compiler framework designed to enable hardware acceleration for Machine Learning (ML) workloads. Glow accepts computation graphs from ML frameworks like PyTorch and generates optimized code for ML accelerators. It features hardware-independent optimizations, a configurable memory allocator, a linear algebra optimizer, and a CPU-based reference implementation for accuracy testing. The goal is to reduce time-to-market for hardware partners and extend hardware acceleration beyond data centers to mobile devices.