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In this inaugural episode of Computing Across Disciplines, we talk with Steve Furber, Professor of Computer Engineering at the University of Manchester. He’s probably most well known for designing the ARM processor, but his more recent research has led him in a surprising new direction: simulating the human brain. In our conversation, we talked about his role on the EU Human Brain Project, his latest architecture SpiNNaker, and his experiences working at the intersection of computer science and neuroscience.

Bio

Steve Furber CBE FRS FREng is ICL Professor of Computer Engineering in the School of Computer Science at the University of Manchester, UK. After completing a BA in mathematics and a PhD in aerodynamics at the University of Cambridge, UK, he spent the 1980s at Acorn Computers, where he was a principal designer of the BBC Microcomputer and the ARM 32-bit RISC microprocessor. Over 100 billion variants of the ARM processor have since been manufactured, powering much of the world’s mobile and embedded computing. He moved to the ICL Chair at Manchester in 1990 where he leads research into asynchronous and low-power systems and, more recently, neural systems engineering, where the SpiNNaker project is delivering a computer incorporating a million ARM processors optimised for brain modelling applications.

Transcript

Andrew Miller:               So, thank you for talking with us. I guess to just get started, I’m interested in what really first got you interested in computers or computer science.

Steve Furber:               Yeah, my background is in mathematics, I started as an undergraduate reading maths, but I’d always had an interest in aviation and flying, and so for my PHD I slid sideways into the engineering department and did a PHD on fairly theoretical aerodynamics. In the course of that I was sort of intrigued by the idea of flight simulation, and sort of got this idea in my head that it might be possible to build a simple flight simulator.

And in following that idea, I heard about a bunch of students who were forming a new university student society called the Cambridge University Processor Group, and that basically was a bunch of students who built computers for fun. And so, I wasn’t a founder of that group, but I got involved from the outset and started building microprocessor-based systems, initially with the objective of using these to implement some kind of flight simulation, but fairly rapidly it became an objective in its own right, I just got interested in the process of building computers from scratch.

Andrew Miller:             Would you classify yourself as a mathematician first, a computer engineer, an electronics engineer, some other something?

Steve Furber:               I think I’m primarily a microchip architect or designer. I still of course use mathematics from time to time, but my formal mathematics is now a long time in the background and I’ve spent quarter of a century in academia, and 10 years before that in computer engineering.

Andrew Miller:             Yeah, so let’s not bury the headline. You were the original designer of the Acorn Risk Machine, ARM processor. I imagine that most of the people listening to this are listening to it on a device that’s powered by that processor. Could you give us just sort of a quick high-level explanation of what distinguishes that design from previous systems?

Steve Furber:               Yes, the ARM grew out of Acorn Computers, which was the small British company that built educational desktop machines, a bit along the lines of Apple in the US. And we got the contract to build the BBC microcomputer, which was the first computer to go in significant scale into UK schools. And based on the success of the BBC micro, we were looking to design a successor machine, and we surveyed the microprocessors that were on the market at the time and for various reasons, we didn’t particularly like the way any of those microprocessors were put together. So we started toying with the idea of designing our own microprocessor.

Now at that time we thought microprocessor design was a bit of a black art, it was done by big semiconductor companies with lots of resource, and Acorn even at its peak was only about 400 people. But then we were introduced to the papers from Berkeley in Stanford on reduced instruction set computing. So we’re talking here about the early ’80s, about 1983, and the RISC papers had been out for a year or two. And here, microprocessors that were designed to have simplified instruction sets and could be put together by a postgraduate class of students in a year.

So we thought this looked like a great line to go. Of course, they were academic prototypes and we were interested in a microprocessor for commercial use, but we put the RISC idea together with some other ideas we had ourselves and came up with the architecture for what was then called the Acorn RISC Machine, RISC was its middle name. And that of course became ARM, and as you say, in quarter of a century since then, ARM has become extremely pervasive.

Andrew Miller:             I’m interested also in your experience. You were in academia and then sort of moved into this corporate environment for lack of a better word for about 10 years, and then now have been back in academia for a significant time. How do you characterize the way that collaboration happens or the way that development happens in those different environments?

Steve Furber:               Yes, I went through from undergraduate to PHD students to research fellow in academia following the conventional path, but towards the end of that, I was beginning to feel a little bit lost in the academic world of research. I wasn’t really feeling sufficiently strongly based to devise my own research programs and so on, and this computer revolution was just starting up and I’d been dabbling in it as a sort of part-time interest. And so when Acorn got the BBC micro-contract, it was a fairly easy decision to switch from academia to industry.

At that time, Acorn was not particularly corporate, it was only about 25 people. And then my attention of course moved from research to very much focusing on development. I enjoyed that industrial environment where the day to day priorities were much clearer to me than they’d been in academia.

10 years later after spending that decade in Acorn, I’d developed; If you like; a foundation of ideas to take forward and I was beginning to get a bit frustrated about the difficulty of getting interesting things going in industry. Acorn had gone from exponential growth to a fairly flat growth profile. And so, moving back to academia, taking those ideas with me became attractive at that point.

Andrew Miller:             And it seems; Just from my brief research; that it looks like at the beginning of this century you made a shift in your research or your career towards this biologically inspired design. I’m wondering if you can give us some insight into what motivated that shift or if you even see that yourself.

Steve Furber:               I very much see that. When I came to Manchester in 1990, I brought these ideas with me, which were very much focused around building asynchronous implementations of the ARM processor, clock-less designs of the ARM, and we spent the ’90s doing this. By the end of the ’90s, I was beginning to get a bit frustrated with conventional computing, processors were a thousand times faster than when I started, but there were still things that young humans find easy that computers struggled to do, such as recognizing faces and so on. So, that lead me to start thinking about the differences between information processing in a computer and information processing in the brain.

As it happened, I was given a small grant, a condition of which was to explore a new direction of research, and I used that grant to explore the idea of inexact associative memories. I’ve always liked associative memories, CAM’s, we use them on a number of ARM designs, but they’re very brittle. You give them exactly the right input and they give you exactly the right output, but you make any mistake in the input and you get nothing useful out.

And so, I started to think about how you could soften that functional characteristic, and what I realized after looking at this for a year or two, was that every angle I took just resulted in me reinventing artificial neural networks. So I thought, “Well if that’s the answer, I’d better go and look into these neural networks properly.” And that set the course if you like for the next 20 years of my research.

Andrew Miller:             Yeah, I’m interested in this moment of what happens when you realize that your research is going in a direction that touches another area or it’s perhaps a new body of kn