4 ms·

I'm always interested in new hardware-oriented DSLs, so great to see effort and work here. rachitnigam, do you have any example designs of "significant" comple

by vrinsd 3y ago

I'm always interested in new hardware-oriented DSLs, so great to see effort and work here. rachitnigam, do you have any example designs of "significant" complexity? I looked over the paper and saw your 2x2 array example -- I'm curious if you make this something like 100x100 or n x m, can that be done in parameterizable way? What about control-structures like bits to enable / disable functionality, etc? Also, do you provide a way to do things like arbitrary insert pipeline registers and let your tools track that? One comment when comparing to Vivado HLS, fundamentally HLS breaks all problems down into CSP-style designs (whether you want that or not) and it takes a lot of experimentation with #pragmas to get a more optimal design. Not claiming Vivado HLS will beat your tool, but it's possible with some tuning to get a "better" design.

rachitnigam 3y ago
Yeah, we've been able to compile entire neural networks like GoogleNet etc. through Calyx. See: https://calebmkim.github.io/files/pubs/src.pdf https://calebmkim.github.io/files/pubs/src.pdf HLS-like languages are an input to Calyx. For example, we're currently building a Vivado alternative using Calyx and the frontend there does the pipelining for us. Calyx then goes in and performs a bunch of other optimizations. A big edge we have is that Calyx supports both statically-scheduled circuits (where the latencies of things are known) and dynamically-scheduled circuits (where latency is not known). Because of this, we have been able to do optimizations in this new flow that take advantage of both.
- vrinsd 3y ago
  This is great to hear. It might be nice to bubble those examples up to your Github page along with some diagrams and logic/resource results after synthesis (i.e. how many LUTs, BRAM, etc). It's easy for people to do a lot of naysaying, it's a lot harder to try and actually do something evolutionary, let alone revolutionary. Please keep posting with up-to-date results.

by vrinsd 3y ago

rachitnigam 3y ago

Yeah, we've been able to compile entire neural networks like GoogleNet etc. through Calyx. See: https://calebmkim.github.io/files/pubs/src.pdf https://calebmkim.github.io/files/pubs/src.pdf HLS-like languages are an input to Calyx. For example, we're currently building a Vivado alternative using Calyx and the frontend there does the pipelining for us. Calyx then goes in and performs a bunch of other optimizations. A big edge we have is that Calyx supports both statically-scheduled circuits (where the latencies of things are known) and dynamically-scheduled circuits (where latency is not known). Because of this, we have been able to do optimizations in this new flow that take advantage of both.

vrinsd 3y ago
This is great to hear. It might be nice to bubble those examples up to your Github page along with some diagrams and logic/resource results after synthesis (i.e. how many LUTs, BRAM, etc). It's easy for people to do a lot of naysaying, it's a lot harder to try and actually do something evolutionary, let alone revolutionary. Please keep posting with up-to-date results.