Use of Analog Computers in Artificial Intelligence (AI)

Analog Computers are a category of units in which bodily portions like electrical voltage, mechanical motions, or fluid strain are represented in order that they’re analogous to the corresponding quantity in the issue to be solved. 

Here is a straightforward instance of an analog laptop.

If we flip the black and white wheels by sure quantities, the grey wheel exhibits the sum of the 2 rotations.
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One of the earliest analog computer systems was The Antikythera  Mechanism, constructed round 100-200 B.C. It concerned a collection of interlocking bronze gears in such a means that the movement of sure dials was analogous to the movement of the solar and the moon. It was additionally succesful of predicting eclipses many years in advance.

Advantages & Disadvantages of Analog Computers

To add two eight-bit numbers, round 50 transistors are required. With an analog laptop, nonetheless, we will add two currents by merely connecting two wires. Similarly, to multiply two numbers, we want 1000s of transistors. On the opposite, we will move a present (I Ampere) by means of a resistor (R-Ohm), and the potential drop throughout the 2 ends of the wire can be I*R, i.e., the product of two numbers.

Analog computer systems are highly effective, quick, and power environment friendly. However, digital computer systems changed them as a result of they’re single-purposed and inaccurate, and for the reason that inputs are steady, it’s troublesome to repeat the method precisely.

Analog Computers & AI.

In AI, analog computer systems had been used for numerous duties, together with sample recognition, decision-making, and management. For instance, they had been used to coach neural networks, that are machine-learning fashions impressed by the human mind’s construction and performance. Analog computer systems had been additionally used to implement rule-based AI techniques that use particular guidelines to make selections or take actions. 

Despite their widespread use in the previous, analog computer systems are not as widespread in AI and machine studying, largely as a result of introduction of digital computer systems. Digital computer systems are a lot quicker and extra dependable than analog computer systems, and so they can retailer and course of a lot bigger quantities of information. Additionally, digital computer systems are simpler to program and keep, which has made them the popular alternative for many AI and machine studying purposes.

The rise in the use of Analog Computers in AI

There is a rising development towards utilizing bigger neural networks in machine studying and synthetic intelligence purposes. This development is pushed by the necessity to enhance efficiency on more and more complicated duties and the supply of extra information, {hardware}, and algorithms to assist the coaching of bigger networks. However, there are particular challenges related to this elevated demand.

Training a big neural community requires an quantity of power equal to the typical yearly consumption of round three households.

Every trendy laptop shops information in reminiscence and accesses it as wanted. But when neural networks require large matrix multiplications, most of the time, the power goes into fetching the worth of the weights moderately than performing the calculations.

According to Moore’s Law, the quantity of transistors on a chip has historically doubled each two years. However, we at the moment are approaching the purpose the place the dimensions of a transistor is approaching the dimensions of an atom, which presents vital bodily challenges to additional miniaturization.

As digital computer systems strategy their limits, neural networks have gained widespread reputation, with a lot of their performance centered on matrix multiplication. Additionally, neural networks don’t require the exact calculations of digital computer systems, as a 98% or 95% confidence in classifying a picture as a canine is enough. These components current a first-rate alternative for analog computer systems to imagine a way more main function in AI.

Case Study: Mythic AI

Mythic AI is an analog computing startup that creates analog chips to run neural networks. Different AI algorithms, like movement detection, depth estimation, classification of objects, and so forth., are run in the analog area.

Mythic has modified digital flash storage cells to make this doable. These cells are usually used for reminiscence storage and might maintain both a one or a zero. A constructive voltage utilized to the management gate permits electrons to move by means of an insulating barrier and grow to be trapped on the floating gate. The voltage can then be eliminated, and the electrons will stay on the floating gate for a very long time, stopping present from flowing by means of the cell. 


The saved worth will be decided by making use of a small voltage. No present will circulation if there are electrons on the floating gate, indicating a zero. If there are not any electrons, present will circulation, which means a one.


Mythic’s thought is to make use of these cells not as on/off switches however as variable resistors. They do that by placing a selected quantity of electrons on every floating gate as an alternative of all or nothing. The higher the quantity of electrons, the upper the channel’s resistance. When making use of a small voltage, the flowing present equals V over R. But you can even assume of this as voltage occasions conductance, the place conductance is simply the reciprocal of resistance. So a single flash cell can be utilized to multiply two values collectively, voltage occasions conductance.

To use this to run a synthetic neural community, they first write all of the weights to the flash cells as every cell’s conductance. Then, they enter the activation values because the voltage on the cells. And the ensuing present is the product of voltage occasions conductance, which is activation occasions weight. The cells are wired collectively in order that the present from every multiplication provides collectively, finishing the matrix multiplication.


Their chip can carry out 25 trillion math operations per second whereas utilizing solely 3W of energy. On the opposite, newer digital techniques can carry out 20-100 trillion math operations per second, however they’re pricey (1000s of {dollars}) and devour 50-100W of energy.

There have been ideas to make the most of analog circuitry in good residence audio system particularly for the aim of detecting wake phrases comparable to “Alexa” or “Siri.” This strategy would require much less energy and permit for the speedy and dependable activation of the digital circuitry in the gadget.

To sum up, it’s unsure if analog computer systems will grow to be as prevalent as digital computer systems. However, they’re higher suited to a range of present duties we wish computer systems to carry out, and possibly we might make machines obtain true intelligence by means of the ability of analog.

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I’m a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I’ve a eager curiosity in Data Science, particularly Neural Networks and their utility in numerous areas.

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