1000X More Efficient Neural Networks: Building An Artificial Brain With 86 Billion Physical (But Not Biological) Neurons
What if in our attempt to build artificial intelligence we don’t simulate neurons in code and mimic neural networks in Python, but instead build actual physical neurons connected by physical synapses in ways very similar to our own biological brains? And in so doing create neural networks that are 1000X more energy efficient than existing AI frameworks?
That’s precisely what Rain Neuromorphics is trying to do: build a non-biological yet very human-style artificial brain.
Which at one and the same time uses much less energy and is much faster at learning than existing AI projects. And that learns, in short, kind of like we meatspace humans do. Plus, that is built with analog chips, not digital.
“We have kind of two missions that are very complimentary: One of them is to build a brain and the other one is to actually understand it,” Gordon Wilson, the soft-spoken but deep-thinking CEO of Rain Neuromorphics told me in a recent TechFirst podcast. “Ultimately, we see these as kind of like Lego pieces that due to their low-power footprint, we’ll be able to concatenate together using things like chiplet integration, advanced packaging, and ultimately scale out these systems to be brain scale — 86 billion neurons, 500 trillion synapses — and low-power enough that they can exist in autonomous devices.”
Wilson seems to be in the habit of very quietly and unassumingly saying things that are essentially completely mind-blowing and world-altering. So quietly you almost miss the gargantuan scale of the scheme.
Which, in this case, is nothing less than the Frankenstein project.
Wilson and co-founders Jack Kendall and Juan Nino started four years ago with a small seed round. Late last year the team taped together a demonstration chip that provesout at least some of their theories about building brain-analog hardware for artificial intelligence workloads via a completely analog chip. And just a month ago the team was rewarded with a $25 million funding round to finish that design, engineer it to be manufacturable, and bring it to market.
One of the investors?
Key to the project is the fact that Rain Neuromorphics is building an analog chip. This is very different than 99.9% of the computer chips on the market that reduce reality as they see it to binary: on or off, zeroes or ones. Those chips have to model the facts and relationships and verbs of computer programs with very precise digital math.
Analog chips, on the other hand, represent reality in a very natural way.
“Digital chips are … built on the very bottom on zeros and ones, on this Boolean logic of on or off, and all of the other logic is then constructed on top of that,” explains Wilson. “When you zoom down to the bottom of an analog chip, you don’t have zeros or ones, you have gradients of information. You have voltages and currents and resistances. You have physical quantities you are measuring, that represent the mathematical operations you’re performing, and you’re exploiting the relationship between those physical quantities to then perform these very complex neural operations.”
How does that work?
By making physics do the work of computation for us, rather than brute-forcing it through a reality-screen of ones and zeroes.
So when you’re building out a neural network and modeling it on how the human brain so incredibly efficiently learns, stores data, and executes decisions, you are more measuring conclusions than arriving at them, using the artificial neurons and synapses that you’ve built.
“In an analog chip … we have the activations of the neurons represented by voltages,” says Wilson. “We have the weights of the synapses represented by resistances, which are held in components called memristors.
