3 ms·

So admittedly I know next to nothing about CNNs and such, but AFAIK, isn't training the difficult, resource intensive part of CNNs? Once you've figured out the

So admittedly I know next to nothing about CNNs and such, but AFAIK, isn't training the difficult, resource intensive part of CNNs? Once you've figured out the coefficients, I think you can implement the pattern matching with an order of magnitude, or less, resources. You don't even need high-precision arithmetic, right?

briga 8y ago
Correct, but most neural networks that do this sort of thing have tens of millions of parameters. Doing this in only 150 KB is extremely memory-efficient, if not exactly high fidelity.
- kenarsa 8y ago
  You are absolutely right. Compressing (using this for lack of better terminology) is extremely important for power-efficient applications as one of the main power draws on a device is external RAM. We had to come up with a bunch of ideas on how to fit our stack into the on-chip RAM (512 KB) and leave enough for OS and the actual application.
  - solarkraft 8y ago
    Do you have some details on how you have done this?
    - kenarsa 8y ago
      Yes, we will publish an article about our speech-to-intent engine and add a link to it on our website. This should happen before the new year. You can find some information about the wake-word engine here https://medium.com/@alirezakenarsarianhari/yet-another-wake-word-detection-engine-a2486d36d8d4 https://medium.com/@alirezakenarsarianhari/yet-another-wake-...
  - Cybiote 8y ago
    This is excellent work. Is there a catch? Limited vocabulary versus open domain? Noise or accent sensitive? How large is the model file? What's the accuracy? It's amazing you were able to get anything beyond keyword spotting on such a relatively low power computer.
    - kenarsa 8y ago
      Thank you. The engine is definitely domain-specific. This would apply to vocabulary and also inference. For example, if you want to use the tech for smart lighting then you would need a different model/context. The demo is done with some noise and there is some reverberation as well. The speakers are also somewhat accented. That being said we would like to open-source a benchmark for this (similar to other products we have). The comment on accuracy is a bit tricky as it would depend on parameters you mentioned and specific task. I will provide more information when we open source the benchmark in Q1 2019.
jdietrich 8y ago
Yes, and it's a very important property of CNNs. You can do the hard computational work of training in the data center, but get results from inferencing at the edge. In a sense, you're using the trained network as an energy storage system, shifting energy requirements away from power- and cost-constrained devices. Inferencing is also highly amicable to hardware optimisation, which we're starting to see in the latest flagship mobile SoCs. I expect to see low-cost microcontrollers with inference accelerators within the next couple of years.
- deleted 8y ago
  [deleted]

briga 8y ago

Correct, but most neural networks that do this sort of thing have tens of millions of parameters. Doing this in only 150 KB is extremely memory-efficient, if not exactly high fidelity.

kenarsa 8y ago
You are absolutely right. Compressing (using this for lack of better terminology) is extremely important for power-efficient applications as one of the main power draws on a device is external RAM. We had to come up with a bunch of ideas on how to fit our stack into the on-chip RAM (512 KB) and leave enough for OS and the actual application.
- solarkraft 8y ago
  Do you have some details on how you have done this?
  - kenarsa 8y ago
    Yes, we will publish an article about our speech-to-intent engine and add a link to it on our website. This should happen before the new year. You can find some information about the wake-word engine here https://medium.com/@alirezakenarsarianhari/yet-another-wake-word-detection-engine-a2486d36d8d4 https://medium.com/@alirezakenarsarianhari/yet-another-wake-...
- Cybiote 8y ago
  This is excellent work. Is there a catch? Limited vocabulary versus open domain? Noise or accent sensitive? How large is the model file? What's the accuracy? It's amazing you were able to get anything beyond keyword spotting on such a relatively low power computer.
  - kenarsa 8y ago
    Thank you. The engine is definitely domain-specific. This would apply to vocabulary and also inference. For example, if you want to use the tech for smart lighting then you would need a different model/context. The demo is done with some noise and there is some reverberation as well. The speakers are also somewhat accented. That being said we would like to open-source a benchmark for this (similar to other products we have). The comment on accuracy is a bit tricky as it would depend on parameters you mentioned and specific task. I will provide more information when we open source the benchmark in Q1 2019.

jdietrich 8y ago

Yes, and it's a very important property of CNNs. You can do the hard computational work of training in the data center, but get results from inferencing at the edge. In a sense, you're using the trained network as an energy storage system, shifting energy requirements away from power- and cost-constrained devices. Inferencing is also highly amicable to hardware optimisation, which we're starting to see in the latest flagship mobile SoCs. I expect to see low-cost microcontrollers with inference accelerators within the next couple of years.

deleted 8y ago
[deleted]