If your taste in TV is anything like
mine, then most of your familiarity with what analog computing looks like probably
comes from the backdrops of something like Columbo. Since digital took over the world,
analog has been sidelined into what seems like a niche interest at best. But this retro
approach to computing, much like space operas, is both making a comeback, and also something
that never really left in the first place. I found this out for myself about a year
ago, when a video from Veritasium sparked my curiosity about analog computing. After that,
I started to read a few articles here and there, and I gotta say…it broke my brain a bit. What
I really wanted to know, though, was this: How can analog computing impact our
daily lives? And what will that look like? Because I definitely don’t
have room in my house for this. I’m Matt Ferrell … welcome to Undecided. This video is brought to you by Surfshark and all
of my patrons on Patreon, but more on that later. Depending on how old you are, you may remember
when it was the norm for a single computer to take up more square footage than your average
New York City apartment. But after the end of the Space Age and the advent of personal computers,
our devices have only gotten smaller and smaller. Some proponents of analog computing argue that
we might just be reaching our limits when it comes to how much further we can shrink. We’ll
get to that in a bit, though. Emphasis on bits. Speaking of bits, this brings us to the
fundamental difference between analog and digital. Analog systems have an infinite
number of states. If I were to heat this room from 68 F to 72 F, the temperature would
pass through an infinite set of numbers, including 68.0000001 F and so on. Digital
systems are reliant on the number of “bits” or the number of transistors that are
switched either on or off. As an example, an 8-bit system has 2^8, or 256 states. That
means it can only represent 256 different numbers. So, size isn’t the only aspect of the
technological zeitgeist that’s changed. Digital computers solve problems in a fundamentally
different way from analog ones. That’s led to some pretty amazing stuff in modern day…at
a cost. Immensely energy intensive computing is becoming increasingly popular. Just look at
cryptocurrencies and AI. According to a report released last year by Swedish telecommunications
company Ericsson, the information and communication technology sector accounted for
roughly 4% of global energy consumption in 2020. Plus, a significant amount of digital
computing is not the kind you can take to go. Just among the thousands of data centers
located across the globe, the average campus size is approximately 100,000 square feet (or
just over 9,000 square meters). That's more than 2 acres of land! Data scientist
Alex de Vries estimates that a single interaction with a LLM is equivalent to “leaving
a low-brightness LED lightbulb on for one hour.” But as the especially power-hungry data centers,
neural networks, and cryptocurrencies of the world continue to grow in scale and complexity…we still
have to reckon with the climate crisis. Energy efficiency isn’t just good for the planet,
it’s good for the wallet. A return to analog computing could be part of the solution. The
reason why is simple: you can accomplish the same tasks as you would on a digital setup
for a fraction of the energy. In some cases, analog computing is as much as 1,000 times
more efficient than its digital counterparts. Before we get into exactly how it works and why
we’re starting to see more interest in analog computers again, I need to talk about another
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I’ve been using Surfshark for years and love it. Link is in the description below. Thanks
to Surfshark, for supporting the channel. And thanks to all of you, as well as my patrons,
who get early, ad-free versions of my videos. So back to how much more energy efficient analog
computing is from its digital counterparts. To understand how that works, exactly, we
first need to establish what makes analog computing…analog. The same way you would make
a comparison with words using an analogy, analog computers operate using a physical
model that corresponds to the values of the problem being solved. And yeah,
I did just make up an analog analogy. A classic example of analog computing is the
Monetary National Income Analogue Computer, or MONIAC, which sounds like a long forgotten car
brand, which economist Bill Phillips created in 1949. MONIAC has a single purpose: to simulate
the Great British economy on a macro level. Within the machine, water represented money as
it literally flowed in and out of the treasury. Phillips determined alongside his colleague
Walter Newlyn that the computer could function with an approximate accuracy of ±2%. And
of the 14 or so machines that were made, you can still find the first churning away
at the Reserve Bank Museum in New Zealand. It’s safe to say that the MONIAC worked
(and continues to work) well. The same goes for other types of analog computers, from
those on the simpler end of the spectrum, like the pocket-sized mechanical
calculators known as slide rules, to the behemoth tide-predicting
machines invented by Lord Kelvin. In general, it was never that analog computing
didn’t do its job — quite the opposite. Pilots still use flight computers, a form of slide
rule, to perform calculations by hand, no juice necessary. But for more generalized
applications, digital devices just provide a level of convenience that analog couldn’t. Incredible
computing power has effectively become mundane. To put things into perspective, an iPhone
14 contains a processor that runs somewhere above 3 GHz, depending on the model.
The Apollo Guidance Computer, itself a digital device onboard the spacecraft
that first graced the moon’s surface, ran at…0.043 MHz. As computer science
professor Graham Kendall once wrote, “the iPhone in your pocket has over 100,000 times
the processing power of the computer that landed man on the moon 50 years ago.” … and we use it
to look at cat videos and argue with strangers. In any case, that ease of use is one of the
reasons why the likes of slide rules and abacuses were relegated to museum displays
while electronic calculators reigned king. So much for “ruling.” But, while digital
has a lot to offer, like anything else, it has its limits. And mathematician
and self-described “analog computer evangelist” Bernd Ulmann argues that we can’t
push those limits much further. In his words: “Digital computers are hitting basic
physical boundaries by now. Computing elements cannot be shrunk much more than today, and there is no way to spend even more
energy on energy-hungry CPU chips today.” It’s worth noting here that Ulmann said
this in 2021, years ahead of the explosion of improvements in generative AI we’ve
witnessed in just the past few months, like OpenAI’s text-prompt-to-video
model, Sora. Which, really disturbs me and I'm very excited by all at the same
time, I need to make a video about that. But what did he mean by “physical
boundaries”? Well…digital computing is starting to bump up against the law.
No, not that kind…the scientific kind. There’s actually a few that are at play
here. We’ve already started talking about the relationship between digital computing
and size, so let’s continue down that track. In a 1965 paper, Gordon Moore, co-founder of
Intel, made a prediction that would come to be known as “Moore’s Law.” He foresaw that
the number of transistors on an integrated circuit would double every year for the
next 10 years, with a negligible rise in cost. And 10 years later, Moore changed his
prediction to a doubling every two years. As Intel clarifies, Moore’s Law isn’t a scientific
observation, and Moore actually isn’t too keen on his work being referred to as a “law.” However,
the prediction has more or less stayed true as Intel (and other semiconductor companies)
have hailed it as a goal to strive for: more and more transistors on smaller and
smaller chips, for less and less money. Here’s the problem. What happens when we can’t
make a computer chip any smaller? According to Intel, despite the warnings of experts in the past
few decades, we’ve yet to hit that wall. We can take it straight from Moore himself, though,
that an end to the standard set by his law is inevitable. When asked about the longevity of his
prediction during a 2005 interview, he said this: “The fact that materials are made of atoms is
the fundamental limitation and it's not that far away. You can take an electron micrograph from
some of these pictures of some of these devices, and you can see the individual atoms of
the layers. The gate insulator in the most advanced transistors is only about three molecular
layers thick…We're pushing up against some fairly fundamental limits, so one of these days we're
going to have to stop making things smaller." Not to mention, the more components you cram
onto a chip, the hotter it becomes during use, and the more difficult it is to cool down. It’s
simply not possible to use all the transistors on a chip simultaneously without risking a
meltdown. This is also a critical problem in data centers, because it’s not only
electricity use that represents a huge resource sink. Larger sites that use liquid
as coolant rely on massive amounts of water a day — think upwards of millions of gallons.
In fact, Google’s data centers in The Dalles, Oregon, account for over a
quarter of the city’s water use. Meanwhile, emerging research on new
approaches to analog computing has led to the development of materials that
don’t need cooling facilities at all. Then there’s another law that stymies
the design of digital computers: Amdahl’s law. And you might be able to get a
sense of why it’s relevant just by looking at your wrist. Or your wall. Analog clocks, the kind
with faces, can easily show us more advantages of analog computing. When the hands move forward on
a clock, they do so in one continuous movement, the same way analog computing occurs in real time,
with mathematically continuous data. But when you look at a digital clock, you’ll notice that it
updates its display in steps. That’s because, unlike with analog devices, digital information
is discrete. It’s something that you count, rather than measure, hence the
binary format of 0s and 1s. When a digital computer tackles a problem,
it follows an algorithm, a finite number of steps that eventually lead to an answer.
Presenting a problem to an analog computer is a completely different procedure, and this cute
diagram from the ‘60s still holds true today: First, you take note of the physical laws
that form the context of the problem you’re solving. Then, you create a differential
equation that models the problem. If your blood just ran cold at the mention of
math, don’t worry. All you need to know is that differential equations model dynamic
problems, or problems that involve an element of change. Differential equations can be used
to simulate anything from heat flow in a cable to the progression of zombie apocalypses. And
analog computers are fantastic at solving them. Once you’ve written a differential equation,
you program the analog computer by translating each part of the equation into a physical part of
the computer setup. And then you get your answer, which doesn’t even necessarily
require a monitor to display! All of that might be tough to envision, so
here’s another analog analogy that hopefully is less convoluted than the labyrinth of wires
that make up a patch panel. Imagine a playground. Let’s say two kids want to race to the same
spot, but each one takes a different path. One decides to skip along the hopscotch court,
and the other rushes to the slide. Who will win? These two areas of the playground are
like different paradigms of computing. You count the hopscotch spaces outlined on
the ground and move between them one by one, but you measure the length of a
slide, and reach its end in one smooth move. And between these two
methods of reaching the same goal, one is definitely a much quicker process than
the other…and also takes a lot less energy. There are, of course, caveats to analog.
If you asked the children in our playground example to repeat their race exactly the same
way they did the first time, who do you think would be more accurate? Probably the one whose
careful steps were marked with neat squares, and whose outcomes will be the same — landing
right within that final little perimeter of chalk. With discrete data, you can make perfect
copies. It’s much harder to create copies with the more messy nature of continuous data. The
question is: do we even need 100% accurate calculations? Some researchers are proposing
that we don’t, at least not all the time. That said, what does this have to do with Amdahl’s
law? Well, we can extend our existing scenario a little further. It takes time to remember
the rules of hopscotch and then follow them accordingly. But you don’t need to remember any
rules to use a slide — other than maybe “wait until there isn’t anybody else on it.” Comment
below with your favorite playground accidents! In any case, because digital computers 1.
reference their memories and 2. solve problems algorithmically, there will always be operations
(like remembering hopscotch rules) that must be performed sequentially. As computer science
professor Mike Bailey puts it, “this includes reading data, setting up calculations, control
logic, storing results, etc.” And because you can’t get rid of these sequential operations,
you run into diminishing returns as you add more and more processors in attempts to speed up
your computing. You can’t decrease the size of components forever, and you can’t increase
the number of processors forever, either. On the other hand, analog computers
don’t typically have memories they need to take time to access. This allows
them more flexibility to work in parallel, meaning they can easily break
down problems into smaller, more manageable chunks and divide them
between processing units without delays. Here’s how Bernd Ulmann explains it In his 2023
textbook, Analog and Hybrid Computer Programming, which contributed a considerable
amount of research to this video: “Further, without any memory there
is nothing like a critical section, no need to synchronize things,
no communications overhead, nothing of the many trifles that haunt
traditional parallel digital computers.” So, you might be thinking: speedier, more
energy-efficient computing sounds great, but what does it have to do with me? Am I going to
have to learn how to write differential equations? Will I need to knock down a wall in my office
to make room for a retrofuturist analog rig? Probably not. Instead, hybrid computers that
marry the best features of both digital and analog are what might someday be in vogue.
There’s already whisperings of Silicon Valley companies secretly chipping away at…analog
chips. Why? To conserve electricity … and cost. The idea is to combine the energy
efficiency of analog with the precision of digital. This is especially important for
continued development of the power-hungry machine learning that makes generative
AI possible. With any hope, that means products that are far less environmentally
and financially costly, to maintain. And that’s exactly what Mythic, headquartered in
the U.S., is aiming for. Mythic claims that its Analog Matrix Processor chip can “deliver the
compute resources of a GPU at 1/10th the power consumption.” Basically, as opposed to storing
data in static RAM, which needs an uninterrupted supply of power, the analog chip stores data
in flash memory, which doesn’t need power to keep information intact. Rather than 1s and 0s,
the data is retained in the form of voltages. Where could we someday see analog computing
around the house, though? U.S.-based company Aspinity has an answer to that. What it
calls the “world’s first fully analog machine learning chip,” the AML100, can act as
a low-power sensor for a bunch of applications, according to its website. It can detect a wake
word for use in voice-enabled wearables like wireless earbuds or smart watch, listen for
the sound of broken glass or smoke alarms, and monitor heart rates, just to name a few. For those devices that always need to be
on, this means energy savings that are nothing to sneeze at (although I guess
you could program an AML 100 to detect sneezes). Aspinity claims that its chip
can enable a reduction in power use of 95%. So, the potential of maximizing efficiency
through analog computing is clear, and the world we interact with every day is itself
analog. Why shouldn’t our devices be, too? But to say that analog programming appears intimidating
(and dated) is…somewhat of an understatement. It’ll definitely need an image upgrade
to make it approachable and accessible to the public — though there are already models out
there that you can fiddle with yourself at home, if you’re brave enough. German company
Anabrid, which was founded by Ulmann in 2020, currently offers two: the Analog Paradigm
Model-1, and The Analog Thing (or THAT). The Model-1 is intended for more experienced
users who are willing to assemble the machine themselves. Each one is produced on
demand based on the parts ordered, so you can tailor the modules to your needs. THAT, on the other hand…and by THAT I mean THAT:
The Analog Thing, is sold fully assembled. You could also build your own from scratch — the
components and schematics are open source. So what do you actually do
with the thing? Y’know…THAT? I’ll let the official wiki’s FAQ answer that: “You can use it to predict in the natural
sciences, to control in engineering, to explain in educational settings, to imitate in
gaming, or you can use it for the pure joy of it.” The THAT model, like any analog computer,
solves whatever you can express in a differential equation. As a reminder, that’s
basically any scenario involving change, from simulating air flow to solving
heat equations. You can also make music! But as analog computing becomes more
readily available, there’s still a lot of work to be done. For one thing,
It’ll take effort to engineer seamless connectivity between analog and digital
systems, as Ulmann himself points out. Until then, what do you think? Should we take
the word of analog evangelists as gospel? Or are we better off waiting for flying cars?
Jump into the comments and let me know. Be sure to check out my follow-up podcast, Still
To Be Determined, where we'll be discussing some of your feedback. Before I go, I’d
like to welcome new Supporter+ patrons Charles Bevitt and Tanner. Thanks so much for
your support. I’ll see you in the next one.
