@masahi I think my effort to create [MetalXLA](https://github.com/philipturner/metal-xla) would be the perfect opportunity to experiment with using AutoTVM to accelerate training. It's a real-time ML context where you have to balance compilation cost with code optimization. Also, you would either compete with or work with MPSGraph, giving a realistic scenario where other framework’s compilers might sometimes be better than TVM. Instead of CUDA XLA or PyTorch, which are relatively established, this backend is very open to change. I could even add features just to help out with TVM experimentation.
Also, the timeframe for when such experimentation will happen is perfect. There’s a several month gap between now and when both S4TF (may) be resurrected and I finish some collaboration with PyTorch on ops such as 3D convolutions. This gives ample time for you and others at TVM to debate whether it’s a good investment. I will also develop MetalSLC*, which is vital data for an AI algorithm concerned with predicting performance. *Can't provide a link because of this forum's restriction on new users. I read this research paper on using ML to predict computational cost of models: https://arxiv.org/abs/1811.11880. That research only focused on NVIDIA GPUs. Several other parties are recently making GPUs with good ML capabilities (Intel, Imagination, Apple) besides NVIDIA. Investing time into experimenting with a Metal project would help break the ML community out of the walled garden of NVIDIA. --- [Visit Topic](https://discuss.tvm.apache.org/t/question-about-what-tvm-does/11775/3) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/0ec20ac4e96e80374a65f8944b433192cc6e0e8519394b9902225be5b697c44b).
