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These are all good questions/points History is something we need to contend with, not just for neural networks, but also for analog computing which has a simil
by Quanticles 11y ago
These are all good questions/points
History is something we need to contend with, not just for neural networks, but also for analog computing which has a similarly troubled past.
For NN history, there has not actually been a market for NN accelerators until recently. You can see this because:
1. No NN algorithm was worth accelerating until AlexNet came along in 2012
2. What commercial products even use NN now? Currently it is mostly just voice recognition which is processed server-side.
Right now we are not attempting to go after any markets that a GPU would be sufficient for the reasons you mention; we're sticking to products that can only work with our technology. By the time we went after an overlapping market our credibility would be established and that wouldn't be an issue.
- p1esk 11y agoYes, the market for NN based products is still in its infancy. It can explode if Apple or Samsung decide to do image or voice processing locally on a smartphone, by using a coprocessor/accelerator chip alongside with CPU/GPU. It could make sense considering the expense (power, time, bandwidth costs) of sending every image off to a datacenter for processing. I'm curious, have you considered using analog weights (e.g. floating gate transistors, or DRAM capacitors)? This could reduce multiplication from 32 transistors to just one!
- Quanticles 11y agoAnalog weights can save a lot of delay/power/cost if you can implement them right, easier said than done