, Polytechnic University of Turin
Abstract—This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Computers.
Keywords—green computing; scientific computing; neurocomputer architecture; microprocessors; ARM; low power; inexact computing
ARM microprocessors are found in nearly every consumer device, from smartphones to gameboxes to e-readers and digital televisions. But did you know that, combined, these same ARM microprocessor cores can simulate the human brain?
The Spiking Neural Network Architecture (SpiNNaker), a massively parallel neurocomputer architecture, aims to use more than one million ARM microprocessor cores to model—in real biological time—nearly one billion spiking neurons.1 The model comes from the University of Manchester's Advanced Processor Technologies Team under the guidance of Steve Furber, an IEEE Fellow and 2013 IEEE Computer Pioneer Award recipient (www.youtube.com/watch?v=x_H_6xG1TEs). Furber's vision is to apply computer engineering techniques to multi-disciplinary research on information processing in the brain.
In 2013, IEEE Transactions on Computers (TC) published Furber and his colleagues' article on the SpiNNaker system's architecture and physical design.1 Furber also prepared a video illustrating the paper's contributions (www.youtube.com/watch?v=EhPpxsK2Ia0). As editor in chief of TC, I invite you to read not only the original paper but also its 2015 follow-up.2 In the most recent paper, Furber and his colleagues describe the innovative SpiNNaker software:
Each low-power ARM core has limited resources, so the SpiNNaker engine uses no more than 90 kilowatts of electrical power.
Furber and his team's research outlines a broader picture in which inexact computing—using less energy and power—could form the backbone of an emerging research and multidisciplinary application area. This topic is increasingly relevant in the context of sustainability and green computing. Their research also stimulates exploration of ways that the axioms of conventional machine design can be drastically changed to achieve different and very ambitious goals. Inspired by Furber and his colleagues, scientists might open themselves to new approaches in which creativity reshapes how computing systems are designed and implemented.