Even before Siri and Google Home became our household companions, we’ve had a tendency to speak of computers in terms of “thinking.” In truth, though, conventional computers really don’t function like brains at all. But computer science is getting closer.
One sign of this is TrueNorth, a 4-square-centimeter chip that possesses some 5.4 billion transistors, and 1 million “neurons” that communicate via 256 million “synapses.” Rajit Manohar, John C. Malone Professor of Electrical Engineering and Computer Science, came to work on the chip with a team of IBM researchers in a years-long collaboration that resulted in TrueNorth. Funded by the Defense Advanced Research Projects Agency (DARPA) as part of its Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program, TrueNorth is a pioneering example of the neuromorphic chip – a new breed of computer circuitry modeled after the brain. It’s the size of a postage stamp and it could be the start of a revolution in how we make and use computers.
Manohar, who started at Yale in January, came to the project while a faculty member at Cornell through his work with asynchronous systems, one of his research specialties. In devices with asynchronous circuits, each computational function is allowed as little or as much time as needed to complete its task. “It’s like a relay race - you hand the baton to the next person when you’re there,” he said. By working asynchronously and in parallel – similar to how neuroscientists believe the brain operates – these functions allow for greater complexity and use much less energy.
It’s a sharp contrast to how most digital devices operate, with their synchronous circuits. In these systems, built-in clocks allow the same amount of time for the completion of each computational function. It’s reliable, but it also means the system can run only as fast as the slowest function in the chain.
“In a clocked implementation, everything has to fit into a time budget, so unless you make everything faster, your chip doesn’t run faster - and ‘everything’ includes things you don’t always need,” Manohar said.
The neurons of TrueNorth work in parallel with each other, each doing what it needs to do to complete a task. They communicate via bursts of electric current, known as spikes. Drawing 70 milliwatts of power – equal to that of a hearing aid – its consumption is miniscule compared to conventional computers performing similar tasks.
Neuroscience has given us a much better understanding of what’s happening in the brain, and that information inspired the architecture of the TrueNorth chip. But it’s a stretch to call TrueNorth a copy of the brain’s functions since we still don’t know exactly how the brain works. That’s one of the things that fascinates Manohar about his work.
“The brain is an asynchronous system that we don’t really understand very well, and it can do certain things that we don’t know how to get computers to do today – and that’s interesting,” he said.
Many efforts in neuromorphic computing are aimed at getting a better understanding how the brain works. The makers of TrueNorth approached their project from the other direction; how can the processes of the brain make for better computing? That also suits Manohar’s interests.
“I’m not in it to understand the biology, I’m in it to understand how it does this computation.”
To see what kind of real-world applications TrueNorth might have, the research team developed a multi-object detection and classification application and tested it with two challenges: one was to detect people, bicyclists, cars, trucks, and buses that appear periodically on a video; the other was to correctly identify each object. TrueNorth proved adept at both tasks.
Even if it captures just a fraction of the human brain’s complexity – according to its makers, the chip has the brain power of a bumblebee – that’s enough to accomplish some remarkable tasks. For instance, it allows users to change the channel without touching the TV or a remote control. Samsung, which has evaluated the TrueNorth chip, announced that it is developing a system in which TV users can control their sets simply by gesturing. Officials at the Los Alamos National Lab have also discussed using it for some supercomputing calculations.
Manohar predicts it won’t be long before this kind of technology ends up in everyday devices.
“These neuro-computing algorithms currently provide state-of-the-art performance for tasks like object detection, recognizing faces – tasks that a lot of companies care about today,” he said. “Imagine having photos or videos that you search for in the same way that you search for text today; these types of chips are way more efficient at that kind of computation.”
Manohar is also the founder of Achronix Semiconductor, a company that specializes in high-performance asynchronous field programmable gate arrays (FPGA) chips. MIT Technology Review listed him as one of their “35 Innovators Under 35” for his work on low-power microprocessor design. His other specialties include low power embedded systems, concurrent systems, and formal methods for circuit design.
It was through mathematics that Manohar came to computer science.
“At some point, I wanted to use mathematics for something more applied,” he said. “I thought computer science was interesting from an applied math perspective – a lot of the techniques and some of the foundations are very mathematical.”
He’s currently working with a team of researchers from the University of Waterloo and Stanford University on a multichip system that Manohar says would be the next step forward in neuromorphics.
“We’d like to demonstrate significantly higher efficiency compared to all the existing platforms – that’s always the goal,” he said. “We think we know how to do that.”
Written by William Weir