# Why do we need nonlinear electronics for computing?

When I read about photonics, I always see that they can be used for linear transformations (just matrix multiplications), and that this is a limitation that makes them unsuitable for building a complete photonic microprocessor. Why are linear transformations insufficient? What kind of computations require nonlinear transformations, and is there a subtopic in EECS that tackles the separation between applications of linear and nonlinear operations?

• Binary addition, for starters. Jun 19, 2022 at 1:04
• Fundamentally the reason that we use nonlinear elements is that most of the things we want to compute are ultimately nonlinear. Think about the basics of any programming language, how would you implement simple things like loops if you can't AND conditions together? Jun 19, 2022 at 2:02
• @user1850479 Most things in general are nonlinear. Linearity is a crutch because it is easy to comprehend. Jun 19, 2022 at 2:06
• Old professor shared a bit of wisdom (goes back to at least the 90's): non-linear physics is like non-elephant biology. (In other words, don't confuse the special case and the general case). Jun 20, 2022 at 9:03
• While the accepted answer is certainly a good one, @user1850479's comment is really the fundamental answer. You can't make a non-linear device out of only linear components. There must be some non-linearity to at least one component. So if you want to do some operation that is non linear with a circuit, you musthave nonlinear components. Jun 20, 2022 at 15:25

Most computers use digital logic. Digital circuits are 'restoring' and minimize (eliminate) signal level error propagation. Analog circuits (which are used for linear transformations) generally add noise (errors) and decrease accuracy as signals propagate, making them unsuitable for complex multi-stage calculations.

Basically, if a logic gate has a signal (voltage level) that is high, or 'nearly high', its output will be even closer to 'perfect'. Similarly for low signals. This means that a logic level will propagate through the logic (with some delays), but at each step it doesn't lose quality -- in fact it improves the precision of the signal.

This comes because the digital logic gates are non-linear. For input signals close to the transition point, it has very high gain -- thus the output signal will be further away from the transition point. Since input and output signal levels span the same range, the corollary of this is that as the input level moves away from the transition point, the output saturates -- i.e. asymptotically approaches the ideal level; this implies that in this condition the gain is very low. Thus the circuit is non-linear.

This means that even to implement linear calculations (e.g. matrix multiplication) only the most basic manipulations are practical (i.e. accurate enough) to implement with analog circuits (e.g. opamp gain or integrators). When linear algorithms are implemented digitally (e.g. floating point computation) it is practical to have millions of variables and billions of calculations -- that is not feasible with an analog implementation.

There are a few cases where analog (linear) implementations can be better -- these are ultra low power circuits where a few transistors can implement a calculation that would require 1000's of digital gates; also extremely high speed circuits (multi-GHz) where digital logic isn't fast enough and some noise (inaccuracy) is tolerated. Examples include the front end of RADAR systems where the initial signal processing and filtering is implemented with analog circuits.

to clarify -- even if the computation is linear, it is generally most robustly implemented with digital electronics (e.g. a computer), likely with floating point representations, and computation using that uses non-linear functions (binary logic operations).

• That's an interesting perspective. The brittle nature of digital signals means that frequency spectra of noise isn't propogated through so although resolution is not infinite, precision is maintained. Jun 19, 2022 at 1:51
• Electronic Digital logic can be made arbitrarily precise (use more bits), Analog computation can be arbitrarily accurate (fine calibration, control temperature of components et.c.)
– Liam
Nov 25 at 13:24

I think you're confusing linear mathematics with linear electronics by attempting to view it all through the lens of abstract mathematics. Or perhaps confusing the method (the logic of the math being performed) with the means (the physical representation of that logic and how the calculation is physically carried out).

Our digital computing is built on ones and zeroes because switches that are either conducting or not conducting are easy to construct in real life. That's it. That's the reason we do the things the way we do it. No need to think about math like linearity transformations.

But it happens to be that a switch is a nonlinear device because the frequency spectra of the signal you get out of the switch is not limited to the frequency spectra of the signal you use to control the switch. But that's we don't use switches because we need nonlinear devices in computing. It's the reverse. We use switches for much less abstract reasons and they just happen to be nonlinear devices.

• I don't think he is confusing the concept of linearity. Rather he is thinking about optical computing where elements are typically VERY linear (photons REALLY like to obey superposition) and thus the types of computation you can do are limited by the need to map them to interactions photons (rather than electrons) can do. A lot of optical computing thus reduces to trying to engineer practical nonlinear optical interactions. I think he is asking why you need than and can't run a typical computer program using only linear operations. Jun 19, 2022 at 2:34
• @user1850479 I see. That's even simpler then...you're boxed in with only linearity since it is a very small subset of everything. You can't get any frequencies out other than those you put in so in a sense you can't make anything "new" signal wise. If everything non-linear is the name of the game you can get close-to-linear which is still nonlinear, but if you have only linear that's the end of the road. There are many ways to be non-linear but only one way to be linear. I guess in most cases non-linearity runs rampant so we usually strive for linearity because it is easy to analyze. Jun 19, 2022 at 3:19
• A computer program (as run on a microprocessor) relies on two assumptions. Discrete time, and discrete value. Neither are properties of a linear optical system.
– Liam
Nov 25 at 13:45

We ask computers to calculate arbitrary functions of their inputs. Most functions are nonlinear. The simplest example is comparison if(a>b) .... A linear computer can't make decisions.

+ 0 1
0 00 01
1 01 10

There are actually two circuits/functions here, one for each bit of the output. The least-significant bit of the sum is represented by the function XOR:

XOR 0 1
0 0 1
1 1 0

and the "carry" output is represented by the function AND:

AND 0 1
0 0 0
1 0 1

Both of these are nonlinear. AND is at least monotone: you could represent it using a linear function of its two inputs and then a thresholding operation — but of course thresholding is nonlinear as detailed by jp314. XOR isn't even that nice: you can never draw a neat line between the inputs that get a 0 output and the ones that get a 1 output.

Adding two numbers of more than one bit requires chaining together the carry-outs of one adder to the input of another adder, so the absence or presence of a one bit at a given point in the output is a very nonlinear function of every less-significant bit of both of the inputs.

Now, is this a consequence of our insistence on binary representation? Sort of. What about analog computers? Well, they're called that because the voltages inside the computer are analogous to the values in the computation. An analog computer with only linear elements could only solve linear problems, and would therefore be rather unexciting.

• I agree with your answer. Funnily enough, XOR is considered linear in the context of GF(2) arithmetic, cryptanalysis, etc. Jun 21, 2022 at 2:36