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Yes, analog computing can be more efficient and faster. But there are reasons why analog was eclipsed by digital. 1. Flexibility. Reprogramming digital computa

Yes, analog computing can be more efficient and faster. But there are reasons why analog was eclipsed by digital. 1. Flexibility. Reprogramming digital computation is easy and quick. 2. Robustness. One can mass-produce devices that operate in the digital domain and sell them as working as spec'd. But operate them in the analog domain, and you will work with a million snowflakes that all operate slightly differently, giving different computation results. And when temperature changes, the result of your analog computation will inevitably change, too. You can work around that by adding more circuitry, and partly also on the algorithmic end but it will cost efficiency and precision. Matrix multiplication on an analog device is great, unless you want an exact and reproducible result.

R0b0t1 5y ago
He touches on this in his first video. The workaround is we are now getting into a regime of computing where exact reproducibility is not necessary. It will take a while though. It is still quite hard to remove noise for opamp circuits.
- periheli0n 5y ago
  > we are now getting into a regime of computing where exact reproducibility is not necessary. I think that is a myth. Predictable, reproducible and explainable outcomes are the holy grail of computing, in particular in AI. If stochasticity is desired, there are methods to inject it, with precise control of the level of stochasticity and the distribution. This level of control is absent in analog computing. Device mismatch introduces some randomness, but it cannot be controlled in practice. Instead of adapting the randomness to the algorithm, one ends up adapting the algorithm to the randomness. Working with analog computers is a fascinating academic exercise. For practical applications, I doubt we’ll see it compete with digital computation anytime soon.
  - R0b0t1 5y ago
    I said exact reproducibility. Every time you add numbers your neurons likely fire in slightly different ways, but the end result is what is reproducible and useful. He's not totally correct because, like I said, the way we think of analog computers right now is likely not going to get us to where we need to be. Noise is a huge problem. Our own neurons do not seem to use continuous voltage, for example, and use pulse density coding, at least for some of their operation.
somat 5y ago
on point 2 another way to put it is that digital computers take much much lower tolerance components than analog computers. A digital computer transistor has to operate(assuming ttl logic) at around 5 volts and 2 volts the transistor behavior in between does not really matter. The analog computer transistor has to operate to a high precision at all voltage ranges. Go ahead and start pricing out high precision transistors and the environmental controls needed to keep them there and you will see why we use digital computers. The history of the navy NTDS air defense system is interesting because they were in a position where they could have gone ether way. use known and understood analog computers or go with the unknown new tech, digital computers. https://ethw.org/First-Hand:No_Damned_Computer_is_Going_to_Tell_Me_What_to_DO_-_The_Story_of_the_Naval_Tactical_Data_System,_NTDS https://ethw.org/First-Hand:No_Damned_Computer_is_Going_to_T...

R0b0t1 5y ago

He touches on this in his first video. The workaround is we are now getting into a regime of computing where exact reproducibility is not necessary. It will take a while though. It is still quite hard to remove noise for opamp circuits.

periheli0n 5y ago
> we are now getting into a regime of computing where exact reproducibility is not necessary. I think that is a myth. Predictable, reproducible and explainable outcomes are the holy grail of computing, in particular in AI. If stochasticity is desired, there are methods to inject it, with precise control of the level of stochasticity and the distribution. This level of control is absent in analog computing. Device mismatch introduces some randomness, but it cannot be controlled in practice. Instead of adapting the randomness to the algorithm, one ends up adapting the algorithm to the randomness. Working with analog computers is a fascinating academic exercise. For practical applications, I doubt we’ll see it compete with digital computation anytime soon.
- R0b0t1 5y ago
  I said exact reproducibility. Every time you add numbers your neurons likely fire in slightly different ways, but the end result is what is reproducible and useful. He's not totally correct because, like I said, the way we think of analog computers right now is likely not going to get us to where we need to be. Noise is a huge problem. Our own neurons do not seem to use continuous voltage, for example, and use pulse density coding, at least for some of their operation.

somat 5y ago

on point 2 another way to put it is that digital computers take much much lower tolerance components than analog computers. A digital computer transistor has to operate(assuming ttl logic) at around 5 volts and 2 volts the transistor behavior in between does not really matter. The analog computer transistor has to operate to a high precision at all voltage ranges. Go ahead and start pricing out high precision transistors and the environmental controls needed to keep them there and you will see why we use digital computers. The history of the navy NTDS air defense system is interesting because they were in a position where they could have gone ether way. use known and understood analog computers or go with the unknown new tech, digital computers. https://ethw.org/First-Hand:No_Damned_Computer_is_Going_to_Tell_Me_What_to_DO_-_The_Story_of_the_Naval_Tactical_Data_System,_NTDS https://ethw.org/First-Hand:No_Damned_Computer_is_Going_to_T...