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Wow! This looks amazingly impressive. Super-duper good. If the software is as impressive as the website (haven't tried it out yet!), you'll make tons of $$$ on

Wow! This looks amazingly impressive. Super-duper good. If the software is as impressive as the website (haven't tried it out yet!), you'll make tons of $$$ on this product. If I were a vc with millions I'd be begging you to take some. I wonder if you plan to support CGRAs and LSTMs? Also, what about quantized and compressed models? Model compression is a sore point and afaik, there aren't any good tools that lets you make tradeoffs between accuracy and compute efficiency.

tdba 5y ago
Thank you for the kind words! Just to clarify, the core technology here is free and open source, anyone can use it right now for free. We do have commercialization plans in addition - we may explore things like additional paid features for enterprise use or paid tiers of extra support. Regarding LSTMs, yes. We're aiming to support all machine learning model architectures: do you have any particular models you're interested in that we should be prototyping with? For CGRAs, we don't have any immediate plans to explicitly support them. What kind of use case do you have in mind? Generally, any platform that can implement a blob of generated RTL should be something we can work with quite easily.
- balsam 5y ago
  I am guessing some kind of vivado replacement is in the works? As it stands now your product seems to be fully offline+CLI - pretty please just tell me how you can get revenue from that?! That said, I am going to get a PYNQ-Z2 just to try this out! Btw, quick glance at the tutorial says Z1.. can I assume Z2 would be barely an inconvenience?
  - tdba 5y ago
    Yes, Pynq Z2 should work just as well (it's the exact same FPGA, just a slightly different board). We've been testing with Z1 which is why I recommended it in the tutorial. For commercialization, the core technology will always be free and open source, but we plan to offer a “pro” version with extra enterprise features under a dual license arrangement, similar to Gitlab. We are also working on a cloud service for running our tools in a hosted setup, in which you’ll be able to run a search across all possible Tensil architectures to automatically find the best FPGA for your model. I'd love to hear your feedback on these plans!
323454 5y ago
Just saw your edit re: model compression. One thing that Tensil can do is help you avoid the need to quantize or compress your model entirely! For example, we've found that using a 16-bit fixed point numeric data type preserves almost all the model accuracy while not sacrificing performance thanks to the huge amount of parallelism available on FPGA. The broader point is that Tensil is extremely flexible, so you can try out lots of different accelerator configurations to find the one that works best for your ML model. Think of it as optimizing the hardware first, then the software if needed. We're actually working on a tool to manage and automate this hardware architecture search - watch this space!

tdba 5y ago

Thank you for the kind words! Just to clarify, the core technology here is free and open source, anyone can use it right now for free. We do have commercialization plans in addition - we may explore things like additional paid features for enterprise use or paid tiers of extra support. Regarding LSTMs, yes. We're aiming to support all machine learning model architectures: do you have any particular models you're interested in that we should be prototyping with? For CGRAs, we don't have any immediate plans to explicitly support them. What kind of use case do you have in mind? Generally, any platform that can implement a blob of generated RTL should be something we can work with quite easily.

balsam 5y ago
I am guessing some kind of vivado replacement is in the works? As it stands now your product seems to be fully offline+CLI - pretty please just tell me how you can get revenue from that?! That said, I am going to get a PYNQ-Z2 just to try this out! Btw, quick glance at the tutorial says Z1.. can I assume Z2 would be barely an inconvenience?
- tdba 5y ago
  Yes, Pynq Z2 should work just as well (it's the exact same FPGA, just a slightly different board). We've been testing with Z1 which is why I recommended it in the tutorial. For commercialization, the core technology will always be free and open source, but we plan to offer a “pro” version with extra enterprise features under a dual license arrangement, similar to Gitlab. We are also working on a cloud service for running our tools in a hosted setup, in which you’ll be able to run a search across all possible Tensil architectures to automatically find the best FPGA for your model. I'd love to hear your feedback on these plans!

323454 5y ago

Just saw your edit re: model compression. One thing that Tensil can do is help you avoid the need to quantize or compress your model entirely! For example, we've found that using a 16-bit fixed point numeric data type preserves almost all the model accuracy while not sacrificing performance thanks to the huge amount of parallelism available on FPGA. The broader point is that Tensil is extremely flexible, so you can try out lots of different accelerator configurations to find the one that works best for your ML model. Think of it as optimizing the hardware first, then the software if needed. We're actually working on a tool to manage and automate this hardware architecture search - watch this space!