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Like the direction you are headed. Considering that use of ASICs is going to rise, think you should consider local installs through docker(like machinebox.io)

Like the direction you are headed. Considering that use of ASICs is going to rise, think you should consider local installs through docker(like machinebox.io) or another technique. Also federated learning would be next thing to take on.

prats226 9y ago
We do provide docker option as well. Federated learning looks like a good way of edge computing + deep learning to offer personalized models as well as use them to improve general model.
fulafel 9y ago
The RPi is an asic based device, no? A web search yields no other applications of BCM2837B0.
- thesmok 9y ago
  No, it's still a general purpose ARM CPU. A good example of ASIC would be a bitcoin mining device.
  - fulafel 9y ago
    Quoth WP: "Modern ASICs often include entire microprocessors, memory blocks including ROM, RAM, EEPROM, flash memory and other large building blocks. Such an ASIC is often termed a SoC (system-on-chip). " The ARM core on the SoC is part of the asic. As is the VideoCore part.
    - prats226 9y ago
      I think what OP meant was ASIC specific to deep learning like TPU's. However as I see, current frameworks are not matured enough to support GPU's and TPU's with exact same code. Also there are no standards so every big org is going to build support for their own ASIC interfaces for the framework they manage. Is there an open source interface for ASIC's for deep learning?
      - mindhash 9y ago
        Yes. I was talking about AI Chips (FPGA and ASICs - NN Processors). https://github.com/basicmi/Deep-Learning-Processor-List https://github.com/basicmi/Deep-Learning-Processor-List https://www.nextbigfuture.com/2017/11/at-least-16-companies-developing-deep-learning-chips.html https://www.nextbigfuture.com/2017/11/at-least-16-companies-.... Cambricon is going big in china so its not just google and apples. They claim to be 6 times faster than GPU. I am more interested in potential of being able to run video processing, voice models effortlessly on tiny devices. and also to train models offline or locally. I think there is a good scope of solutions (like vision recognition) that port well across AI chips.
      - mindhash 9y ago
        >> Also there are no standards so every big org is going to build support for their own ASIC interfaces for the framework they manage. This is true. Google has TF for tpu and Intel Nervana has neon. Each player will likely publish a software library.

prats226 9y ago

We do provide docker option as well. Federated learning looks like a good way of edge computing + deep learning to offer personalized models as well as use them to improve general model.

fulafel 9y ago

The RPi is an asic based device, no? A web search yields no other applications of BCM2837B0.

thesmok 9y ago
No, it's still a general purpose ARM CPU. A good example of ASIC would be a bitcoin mining device.
- fulafel 9y ago
  Quoth WP: "Modern ASICs often include entire microprocessors, memory blocks including ROM, RAM, EEPROM, flash memory and other large building blocks. Such an ASIC is often termed a SoC (system-on-chip). " The ARM core on the SoC is part of the asic. As is the VideoCore part.
  - prats226 9y ago
    I think what OP meant was ASIC specific to deep learning like TPU's. However as I see, current frameworks are not matured enough to support GPU's and TPU's with exact same code. Also there are no standards so every big org is going to build support for their own ASIC interfaces for the framework they manage. Is there an open source interface for ASIC's for deep learning?
    - mindhash 9y ago
      Yes. I was talking about AI Chips (FPGA and ASICs - NN Processors). https://github.com/basicmi/Deep-Learning-Processor-List https://github.com/basicmi/Deep-Learning-Processor-List https://www.nextbigfuture.com/2017/11/at-least-16-companies-developing-deep-learning-chips.html https://www.nextbigfuture.com/2017/11/at-least-16-companies-.... Cambricon is going big in china so its not just google and apples. They claim to be 6 times faster than GPU. I am more interested in potential of being able to run video processing, voice models effortlessly on tiny devices. and also to train models offline or locally. I think there is a good scope of solutions (like vision recognition) that port well across AI chips.
    - mindhash 9y ago
      >> Also there are no standards so every big org is going to build support for their own ASIC interfaces for the framework they manage. This is true. Google has TF for tpu and Intel Nervana has neon. Each player will likely publish a software library.