
Analog-Cartpole: A hybrid analog/digital reinforcement learning experiment - ghosthamlet
https://github.com/sy2002/ai-playground/tree/master/analog
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rough-sea
Neural networks are largely operating on 32 or 64 bit floating point numbers.
Many millions of them. If you think how this works at the hardware level there
are many little cores in a GPU for operating on matrices of these numbers. To
feed a 32 bit float into one of these, it requires 32 wires - deep in the
silicon.

Also note that neural networks are inherently noisy. We often insert a bit of
noise into various parts of the computation graph.

In analog circuity only a single wire (or maybe two if you’re using a
differential pair) would be needed to represent a noisy float.

If we had some sort of IC that could dynamically configure large analog
computations, it may allow NN compute-graph computations to be improved by
orders of magnitude. 1 wire instead of 32. Real noise instead of artificial.

Have people ever tried to something like an analog FPGA?

~~~
rrss
Yes. [https://en.wikipedia.org/wiki/Field-
programmable_analog_arra...](https://en.wikipedia.org/wiki/Field-
programmable_analog_array).

There are sort of a lot of papers going back decades that describe various
analog implementations of neural networks.

Some of the challenges:

1\. dynamic range.

2\. randomness. neural networks implemented digitally may have injected noise,
but it is chosen and can be removed or controlled so that e.g. repeated
inferences use the same noise.

3\. multipliers.

4\. digital-to-analog and back. the inputs and outputs will almost certainly
go to and come from digital systems.

5\. training with 'real' nonlinearities. The nonlinearities used in digital
neural networks are perfect, in an analog implementation they would be
imperfect and demonstrate PVT variation.

6\. flexibility. need to have circuits that are parameterized, and some way to
program (and therefore store) the weights.

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yters
I don't get the interest in analog. Whatever the secret sauce that makes Real
Intelligence (TM), analog is not it.

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ganzuul
This might be useful for reducing the "reality gap" in robot simulation.

