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Things I wish I had learned at university: - measuring the efficiency of an algorithm in watts - difference between classical math and computation - relationship between geometry and number theory
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In practice it often depends on things like implementing your multiplication with a lookup table. I think the Landauer limit would matter if we did best effort computing and could harness the actual amount of determinism we can get from the substrate. We are far from that.
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I recall seeing some literature on a concept called “reversible computing” that seemed to be about this too. I guess that’s the synthesis side of power efficiency analysis.
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Reversible computing builds on the realization that entropy results from deleting bits. By building gates that permute bits only, you may be more efficient (but you'll still have to flush out the superfluous bits at some point). Best effort computing is largely unrelated.
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Okay I got lost around the quarks point and this is generally above my CS paygrade but it does look fascinating. So the stochastic cellular automaton simulation is a model of something like a non-robust memory with probabilistic guaranteed state persistence under failure?
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Yes, it is an attempt at building a software paradigm that does not rely on guaranteed determinism of the substrate, but on adaptively recruiting as much determinism as needed for the task. This would allow us to reduce energy consumption by many orders of magnitude.
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