Neuromorphic Computing AND THE FUTURE OF A.I.

BIO-INSPIRED COMPUTER CHIPS WITH DR ALEX MARCIREAU

I’m calling neuromorphic computing the most important computer engineering research in the world, now and through the next 20 years. That’s right, more important than quantum computing (you heard it hear first!). Why? Because everything we want to do in the future of AI, everywhere we want to go long-term, is predicated on transitioning to more fit-for-purpose computer architectures, and the most fit-for-purpose architectures are most certainly those inspired by nature.

MEETING DR ALEXANDER MARCIREAU

I interviewed the irrepressible Dr Alexandre Marcireau at the International Centre for Neuromorphic Systems (ICNS) at University of Western Sydney. Alex is softly-spoken, laughs easily, and as you would expect is extremely passionate about his field. He generously took me on a tour of his lab to check out his prototypes, including the neuromorphic cameras (see right-hand image above, and the cover image for this article) that are currently circulating in the International Space Station. Afterwards he sat down to answer questions and share his views on the future. He’s very much tuned-in to both the immediate applications and the long-term planetary-scale benefits his technology has to offer. I know you’ll enjoy listening to him!

CHECK OUT THE PODCAST TRANSCRIPT →

THE FUTURE OF AI IS ANALOG (YES, REALLY!)

Alex’s long-term dream is analog AI computing from end to end. Biology is messy, every organism is different, but it WORKS SO WELL! If we want to truly emulate the efficiencies of nature in computers that sense and learn then we MUST go analog not digital. We are slicing off and solving one sensory processing function at a time — a bio-inspired camera here, a bio-inspired microphone there — but the long-term dream is end to end. Of course we’ll still be using fast number-crunching and general purpose computing chips for everything else, so the future of computing more broadly will always be a mix of digital and analog, classical and neuromorphic.

And when it comes to the eye-watering energy demands of AI, the comparative advantages with respect to classical chips for AI-related functions are immense. If things keep going as they are, the ruinous energy demands of crypto-mining will be as nothing compared to the future energy demands from exponential AI. Conclusion: long-term we will HAVE TO transition AI workloads off traditional architectures.

ENDLESS REAL-WORLD APPLICATIONS

I loved Alex’s discussion of the different opportunities at the ‘edge’ and the ‘centre’ of AI, and the lab’s real-world applications in deploying high-efficiency low-energy event-based cameras to detect lightning strikes and satellites from the International Space Station, and to track koalas and insects in forests down here on earth. We also had fun talking about using AI to decipher animal languages, such as they are doing at projectceti.org (decoding the communications of sperm whales), and neuromorphic applications to make drones vastly more capable and to enable the bi-directional brain interfaces and neuro-prosthetics I’ve been looking at lately in the future of medicine. So many good things to be transformed.

BIOLOGY + COMPUTING = MAGIC

The field crosses many boundaries. Biologists, mathematicians, hardware engineers, physicists, programmers are all working together to create a new industry – what could be more exciting than that? And what about the big gaps in our understanding of various neural systems in nature, and how every improvement in our biological knowledge (such as those auditory, visual, olfactory and learning connectomes that we keep extending for fruit flies and mice) yields new opportunities? I loved Alex’s honesty when he said that the computer scientists were benefiting immensely from the biologists, but perhaps not yet giving back nearly as much. When I see all the ways AI is being used to unravel the biology of animals and plants, I don’t think it will be one-way traffic for long!

HARDWARE PLUS WETWARE?

As a side note, I enjoyed hearing Alex’s comments about the use of living cells, aka ‘wetware.’ I can’t remember whether this made it onto the recorded interview or not, but when I asked about growing live neurons as a way to resolve some of the challenges of constructing massively parallel data connectors between all those pixels and all those ‘transistors,’ Alex confirmed that this is something scientists have indeed tried, although their experiments have been hampered because the computer quickly dies … literally! When I link these experiments to the work I’m seeing with brain organoids in regenerative medicine, it really opens up my thinking about the long-term (>30 years?) possibilities. Fascinating!

HUGE OPPORTUNITY SPACE

A big takeaway from this interview is how much headroom there is. It’s like looking at the nascent computer chip industry circa 1950. We’ve only started knocking off the little opportunities. As fast as we explore, we identify new ones. The opportunity space is HUGE. If you are investing in computer engineering, or studying it, or building AI systems (who isn’t?) or you happen to manufacture computer chips, take note!

The software side is a particularly innovative space. How to best to receive all that parallel data? How best to process it? How best to navigate the analog/digital interfaces? How can we take full advantage of super-fast and super-local AI decision-making ‘at the edge.’ With so many different possibilities and no baked standards (because, hey, it’s still way too early for those) this field is a boon for creative and talented programmers.

fundamental to the future of ai

When I say more important than quantum, I mean it. Don’t get me wrong, quantum is big. It matters. It tackles big problem spaces, especially in molecular, atomic and sub-atomic simulations that will give us access to new drugs, enzymes and materials, and in complex system optimisation problems. But neuromorphic engineering has the potential to boost the capabilities of EVERY aspect of the biggest technological force of change in our time, artificial intelligence, and further, transitioning to bio-inspired neuromorphic hardware is fundamental to the long-term future of AI if we want it to stay on its current exponential adoption curve without smashing into an energy ceiling.