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SETUN fascinates me as well.

What are your thoughts on ternary computation and neural nets, or general use of it? I myself want to explore some more use cases with utility rather than yak/shaving.

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Neural nets usually use 32-bit floats for weights. There is a rapid trend of using quantization. There are already papers describing 2-bit quantization strategies – either quantization-aware training or post-training quantization. Less precision means larger & faster models (current utility), buuuut more importantly you can start to think about different substrates for implementation – analog or optical seems to be viable paths.

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