LONDON — There has been a tremendous amount of research in recent years into brain-inspired computing to tackle the explosion in computing performance and memory requirements to meet growing demands for artificial intelligence and machine learning in just about everything.

That research is now starting to bear fruit, with at least one neuromorphic computing chip developer, BrainChip, planning to detail its chip architecture next month.

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Earlier this year, Barbara de Salvo, CEA-Leti’s chief scientist explained the semiconductor industry could take its cue from biology to address the power requirements that traditional computing architectures now struggle to meet. She outlined the characteristics of a brain synapse, containing both memory and computing in a single architecture, that can form the basis for brain-inspired non-von Neumann computer architecture. One recent trend in neuromorphic computing is to encode neuron values as pulses or spikes.

And then there’s the European Human Brain Project’s neuromorphic computing project, which has been working on constructing two large-scale, unique neuromorphic machines and prototyping the next generation neuromorphic chips. It recently published a paper on its first full scale simulations of a cortical microcircuit model of 80,000 neurons and 300 million synapses based on the SpiNNaker hardware to demonstrate its usability for computational neuroscience applications.

Markus Diesmann

Professor Markus Diesmann, co-author of the paper and head of the computational and systems neuroscience department at the Jülich Research Center in Germany, said, “There is a huge gap between the energy consumption of the brain and today’s supercomputers. Neuromorphic (brain-inspired) computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics.”

He adds, “It is presently unclear which computer architecture is best-suited to study whole-brain networks efficiently. The European Human Brain Project and Jülich Research Centre have performed extensive research to identify the best strategy for this highly complex problem. Today’s supercomputers require several minutes to simulate one second of real time, so studies on processes like learning, which take hours and days in real time, are currently out of reach.”

The microcircuit model is simulated on a machine consisting of six SpiNN-5 SpiNNaker boards, using a total of 217 chips and 1,934 ARM9 cores. Each board consists of 48 chips and each chip of 18 cores, resulting in a total of 288 chips and 5,174 cores available for use. Of these, two cores are used on each chip for loading, retrieving results and simulation control. Of the remaining cores, only 1,934 are used as this is all that is required to simulate the number of neurons in the network with 80 neurons on each of the neuron cores.

“This is the first time such a detailed simulation of the cortex has been run on SpiNNaker or on any neuromorphic platform,” said Steve Furber, another co-author and professor of computer engineering at the University of Manchester, U.K. “The simulation described in this study used just six boards — 1% of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board.”