tvm

Note that TVM (https://github.com/dmlc/tvm) is a similar system (the IR is a fork of the Halide IR, but substantially improved), and is much more suited to deep learning, with tools like: - TOPI, a library of optimized routines for common deep learning operations - Importers for TensorFlow, ONNX (PyTorch/Caffe2), Keras, MXNet, DarkNet and other frameworks. - AutoTVM for automatically finding fast schedules for arbitrary devices, much faster than random search or the automatic schedules generated by e.g. Halide. - Various mobile GPU runtimes (OpenGL, OpenCL, Vulkan, etc), compared to Halide - Large community contributing optimized runtimes (e.g. Intel + Amazon contributing CPU improvements, improved schedules/declarations, etc) Highly recommend checking it out.

Current tally of high-performance, deep-learning-oriented DSLs/IRs/compilers, in no particular order: - TensorComprehensions (Facebook): https://github.com/facebookresearch/TensorComprehensions - XLA (Google): https://www.tensorflow.org/performance/xla/ - taco (MIT): http://tensor-compiler.org/ - DLVM (UIUC): http://dlvm.org/ - nGraph (Intel): http://ngraph.nervanasys.com/docs/cpp/ - TVM (DMLC): https://github.com/dmlc/tvm Honorable mention to Julia (http://julialang.org) as well.