Thanks for your reply @areusch !
[quote="areusch, post:2, topic:9548"] Is tensorization an option here, or do you need to do more with the TIR after schedule generation? [/quote] Yes, i'm currently trying to use tensorization to map entire convolutions and data preparation steps (data layout, padding) to a HWLib function call, but the process hasn't been particularly smooth for such coarse computations i'm afraid. [Getting data to be transformed from TVM seems suboptimal.](https://discuss.tvm.apache.org/t/te-using-reshape-without-copy/9480?u=jossevandelm) Also creating large tensorization intrinsics is tricky; Right now for example it looks like I would have to generate a separate TIR pass, because I can not merge e.g.`Relu(Conv(Pad(ChgDataLayout(input)),filter))` into one intrinsic; [tensorize/tir does not allow for creating an intrinsic with nested computations](https://discuss.tvm.apache.org/t/tensorize-how-to-use-tensorize-for-composition-op-eg-conv-relu/2336?u=jossevandelm) The TIR pass i'm envisioning could detect those sequential operations and maybe merge them into one as a workaround for this problem. I'm not sure how to write a TIR pass yet, but what I would like to do in the future is to maybe skip some data layout transformations automatically. Now the data has to be transformed every time it is sent from and to the accelerator just because most standard convolutions expect NCHW to work in relay for example. We should not be doing data layout transformations if two consecutive operations are performed on the accelerator. I'm not sure if it would be best to implement this as a Relay pass or a TIR pass. If anyone can confirm that this is possible or can send me some work on this that would be very great, as i've not had time to look into creating my own pass. At some point I'd also like to include some autotuning in the grand scheme of things (probably not with actual timing measurements, but rather with a representative model). But I haven't had time to look into this, and how much effort it would take me to implement this. I'm also afraid the gains of autotuning with coarse tensorization might be quite minimal. But maybe there might be some gains possible for the RISC-V scheduling, i'm not sure. [quote="areusch, post:2, topic:9548"] It seems like you could have a TIR pass that replaces free variables with constants after doing that computation. [/quote] Okay I'll be sure to look into this! Also thank you very much for including other people in the discussion! --- [Visit Topic](https://discuss.tvm.apache.org/t/feedback-on-tvm-port-to-custom-accelerator/9548/3) 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/d35556e8f52c910c8d2fd2a269273b37c1c84093f88a5eca14384f67c991dce0).
