TiGrIS, a tiling compiler that fits ML models onto embedded devices
Ahead-of-time compiler that fits ML models on embedded devices with hard memory budgets. - raws-labs/tigris
TiGrIS, a tiling compiler that fits ML models onto embedded devices launched on April 29, 2026 with 20 votes, #1,003 of 21,248 launches that month and more than 90% of that year's launches.
In plain words
TiGrIS is a tiling compiler designed to fit machine learning models onto embedded devices. It handles the technical challenge of restructuring ML models so they can run on hardware with limited resources. The tool is aimed at developers working with embedded systems who need to deploy ML capabilities without extensive model optimization expertise. It is available as open source on GitHub.
written from the facts on this page · September 2026
From the sources
Tiled Graph Inference Scheduler. An ahead-of-time compiler that tiles ML models to fit embedded devices with hard memory budgets. Give it an ONNX model and a memory budget. It partitions the compute graph into stages, tiles spatial operations, and emits a flat binary plan that the tigris-runtime executes with zero dynamic allocation. On an embedded device with a few hundred KB of SRAM, most interesting models simply don't fit. The usual answer is to shrink the model: quantize harder, prune, pick a smaller architecture, and hope the accuracy hit is acceptable. TiGrIS takes the other approach. It keeps the model you trained and rearranges the computation so that only a small working set lives…from github.com
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