Building an analog computer

In summary, the conversation discusses the use of analog computing in self-learning AI. While it is a popular option, it is not the most practical as digital processors are faster at emulating the necessary algorithms for AI. Building an analog computer involves modeling the circuit in SPICE and optimizing it before implementation. There are also drawbacks to using analog computing, and it is not the most efficient solution.
  • #1
TL;DR Summary
Anyone with experience building electronic analog computers
I am researching self-learning AI, and one of the most talked of solutions is returning to analog computing. Analog computing uses real time changes in voltage and frequency to run calculations or perform functions. Does anyone have any experience in building electronic analog computers based on the op amp?
 
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  • #2
Hello @The Art of DHT ,

:welcome: ##\qquad## !​

Yes, lots of folks have lots of experience :biggrin:.

That may answer your question, but my guess is that it doesn't help you very much :smile:.

google is your friend. Tried some terms too ?

What is it you want to do ?

##\ ##
 
  • #3
The Art of DHT said:
I am researching self-learning AI, and one of the most talked of solutions is returning to analog computing.
That is the talk, but not the practice. It is faster to emulate the AI algorithm on a digital processor. Op-amps are good at linear operations, but AI requires non-linear functions, which are more quickly and more accurately emulated by the multiply-accumulate function in a digital processor.

The first step in building an analog computer is to model the analog circuit in SPICE. Only once the algorithm and circuit has been optimised should a parallel analog solution be implemented.
 
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  • #4
The Art of DHT said:
AI ... one of the most talked of solutions is returning to analog computing.
The AI related analog computing (the one I know about) is not exactly the opamp kind, and honestly, I would rather take that as some kind of hybrid or unique solution than 'real' analog.

Analog computing had some strong points way back, but there was also serious drawbacks too.
By now it's simply more pain than gain.

I's just like the gold standard. Everybody knows why it was phased out, but still, some people just can't stop flirting with the idea.
 
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1. What is an analog computer?

An analog computer is a type of computing device that uses continuously varying physical quantities, such as electrical or mechanical quantities, to represent and manipulate data. Unlike digital computers, which use binary digits (bits) to represent data, analog computers use physical quantities to directly represent and solve mathematical equations.

2. How does an analog computer work?

An analog computer works by using electrical or mechanical components, such as resistors, capacitors, and gears, to represent and manipulate data. These components are connected in a circuit or system that mimics the mathematical equations being solved. By continuously varying the physical quantities, the analog computer can solve complex mathematical problems in real-time.

3. What are the advantages of using an analog computer?

Analog computers have several advantages over digital computers, including their ability to solve complex mathematical equations in real-time, their low cost and simplicity, and their ability to handle continuous data. They are also more resistant to external interference and can perform calculations faster than digital computers in certain applications.

4. What are the limitations of analog computers?

Analog computers have several limitations, including their inability to store and process discrete data, their susceptibility to noise and errors, and their limited precision. They also require specialized knowledge and skills to design and operate, making them less accessible than digital computers.

5. What are some real-world applications of analog computers?

Analog computers have been used in a variety of fields, including engineering, physics, and economics. They have been used to model and solve problems in fluid dynamics, electrical circuits, and control systems. They have also been used for weather forecasting, nuclear physics simulations, and economic forecasting.

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