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takyon writes:

A new brain-inspired architecture could improve how computers handle data and advance AI

IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. Their designs draw on concepts from the human brain and significantly outperform conventional computers in comparative studies. They report on their recent findings in the Journal of Applied Physics, from AIP Publishing.

[...] The IBM team drew on three different levels of inspiration from the brain. The first level exploits a memory device's state dynamics to perform computational tasks in the memory itself, similar to how the brain's memory and processing are co-located. The second level draws on the brain's synaptic network structures as inspiration for arrays of phase change memory (PCM) devices to accelerate training for deep neural networks. Lastly, the dynamic and stochastic nature of neurons and synapses inspired the team to create a powerful computational substrate for spiking neural networks.

[...] Last year, they ran an unsupervised machine learning algorithm on a conventional computer and a prototype computational memory platform based on phase change memory devices. "We could achieve 200 times faster performance in the phase change memory computing systems as opposed to conventional computing systems." Sebastian said. "We always knew they would be efficient, but we didn't expect them to outperform by this much." The team continues to build prototype chips and systems based on brain-inspired concepts.

Biosensor response from target molecules with inhomogeneous charge localization (DOI: 10.1063/1.5036538) (DX)

Previously: IBM Chip Processes Data Similar to the Way Your Brain Does
IBM Builds New Form of Memory that Could Advance Brain-Inspired Computers
Simulating Neuromorphic Supercomputing Designs
The Second Coming of Neuromorphic Computing
Novel Synaptic Architecture for Brain Inspired Computing

Original Submission