Re:How to Set Up an OpAmp Circuit to Do Complex Mathematics
by aswanson on Friday February 17, 2017 @08:27PM
(#13673105)
Those are awesome ideas, ones I never would have thought of! I was thinking more along the lines of using analog filtering for convolutional layers of deep neural networks, dropout layers and filter templates for 2-d features are just bandpass filters, which analog filters can approximate easily. That analog fpga idea is a good one also; there needs to be a way to perform rapid experimentation on these types of ideas. Are there any products/companies you know of in this area?
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Re:How to Set Up an OpAmp Circuit to Do Complex Mathematics
by nickpsecurity on Friday February 17, 2017 @10:39PM
(#13673479)
Your ideas are possibly better just because you already have a chance of building them if they're that close to existing analog schemes. I'd try them first to get a feel for the whole thing.
Far as reprogrammable, type in what I told you into Google: Field-Programmable, Analog Arrays (FPAA's). Or reconfigurable analog. You'll get companies and CompSci. Also remember that analog works best/easiest on oldest process nodes. That combined with multi-project wafers can get your ASIC prototypes down to thousands of dollars rather than tens to hundreds. Middle ground company is a structured ASIC that's basically like a FPGA programmed with 1-2 masks at fab time. Triad Sdmiconductor does that for digital+analog with claimed $400k per design with few weeks turn around. Idk if their analog cells are suitable, though.
Also, for good measure you might enjoy at least looking at the last general-purpose (programmable), analog computer:
Re:How to Set Up an OpAmp Circuit to Do Complex Mathematics
by p1esk on Friday February 17, 2017 @10:54PM
(#13673520)
Here is a neural network using analog weights and neurons:
https://arxiv.org/abs/1610.02091