tdba

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91.
by tdba 5y ago
If your evidence is only anecdotal then I think you need to go find some better evidence or go back and rethink your argument. Calling voucher systems a "scam" based on this reasoning is a big stretch. There are lots of other ways
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by tdba 5y ago
Do you have any evidence to back that up? I see no reason why students need to literally be in the same classroom all day with disabled people to learn empathy for them. There are many other ways they could interact with them without forcin
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by tdba 5y ago
You're arguing against a straw man: most existing voucher programs are means-tested already (ie. only available to your "high cost" students) which negates your argument that the low cost students will use vouchers leave the
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by tdba 5y ago
It depends on the application. For some use cases, moving to a GPU makes total sense. However, if you have power constraints, form factor constraints, performance constraints or simply want to be in control of your own hardware, using an FP
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by tdba 5y ago
I'm not completely sure if the answer is no but I have had difficulty finding anything like that.
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by tdba 5y ago
If you have a device with known performance in mind, you can compare against our benchmarks listed here https://www.tensil.ai/docs/reference/benchmarks/ We'll be expanding this list and adding more compa
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by tdba 5y ago
It's true that inference is still very often done on CPU, or even on microcontrollers. In our view, this is in large part because many applications lack good options for inference accelerator hardware. This is what we aim to change!
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by tdba 5y ago
There are four big categories of ML accelerators. You already familiar with CPUs and GPUs; then there are FPGAs, which offer better performance and efficiency while remaining flexible. Finally there are ASICs (of which the TPU is an example
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by tdba 5y ago
Awesome, I think your use case would make a lot of for Tensil. Looking forward to chatting more! The core technology will always be free and open source, so to commercialize Tensil we're planning to offer a "pro" version whic
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by tdba 5y ago
I replied to your other comment here about commercialization https://news.ycombinator.com/item?id=30652150 I hope it was helpful!
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by 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
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by tdba 5y ago
Sure, we'll check it out!
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by tdba 5y ago
Wow, awesome project! This is exactly the kind of thing we had in mind when we built Tensil. I'd be very curious to hear what happens if you make a v2 perhaps using Tensil for comparison.
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by tdba 5y ago
Definitely! You can see some of our benchmarks here, and we'll be expanding this list soon https://www.tensil.ai/docs/reference/benchmarks/
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by tdba 5y ago
You're very welcome! Stay in touch - I've listed some contact methods here and there in the thread, and we'd love to hear from you again.
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by tdba 5y ago
Will do - as I mentioned in another comment, it can be a bit subtle to find an apples-to-apples comparison, but we'll soon add some cross-platform that we think are reasonable.
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by tdba 5y ago
Thank you - we'd love to see more OSS support from FPGA vendors too and we'll be watching closely for any developments there.
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by tdba 5y ago
Thanks, we'll take a look!
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by tdba 5y ago
Yep, this is something we've heard before. If you're really familiar with the Xilinx ecosystem, one way we've described Tensil is that it is the "Microblaze for ML" - easy to use, lots of flexibility and customizabi
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by tdba 5y ago
Absolutely, the UX for compiler tools often leaves a lot to be desired. This is something we want to fix!
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by tdba 5y ago
Generally the comparison between Tensil and any fixed ASIC is going to run along similar lines, which we explain in this comment regarding the Coral accelerator: https://news.ycombinator.com/item?id=30643520#30645318 The bi
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by tdba 5y ago
Glad this helped clarify things for you! The tricky thing about benchmarks is that one of the key benefits of Tensil is the flexibility to find a trade-off between performance, accuracy, cost and power usage that works for you. Benchmarks
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by tdba 5y ago
This is a great idea, we're looking at boards that could be used in combination with a Raspberry Pi. The reason we haven't investigated this so far is that most of the dev boards we've tested with have an ARM core embedded in
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by tdba 5y ago
Agreed, this is the way things seem to be trending. We'll definitely add support for transformers in the near future, the question is only whether there are other things we should work on first, especially with respect to the edge and
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by tdba 5y ago
Coral is a great project, especially if you are using a completely vanilla off-the-shelf model. However if you've ever tried compiling a custom ML model for it, you know how finicky it can be. There are lots of ways that you can accide
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by tdba 5y ago
In our current demos, the Tensil logic talks to the host through a couple of AXI and AXI Stream interfaces. There are AXI adapters for many other protocols, including PCIe, that should be able to support many different kinds of connectivity
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by tdba 5y ago
We're working on our roadmap right now and prioritizing support based on user interest. If there's a particular model or set of models you're interested in accelerating, I'd love to hear about it! If there's a lot o
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by tdba 5y ago
VMAccel looks very interesting! Send me an email and we can explore how to collaborate.
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Launch HN: Tensil (YC S19) – Open-Source ML Accelerators
96 points by tdba 5y ago|87 comments
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by tdba 5y ago
Thanks! The general answer is that it depends on your model and on which FPGA platform we're talking about, but in a head-to-head benchmark test you'll find results in the ballpark of 2-10x CPU and 0.5-2x GPU. As you point out, th

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