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arulatas writes:

A new way of creating a neural network using specially formulated memristors has been described by a team of researchers from Stony Brook University and the University of California Santa Barbara. The process has the potential to place an entire neural network on a single chip:

The system produced by the authors here involved only a 12-by-12 grid of memristors, so it's pretty limited in capacity. But Robert Legenstein, from Austria's Graz University of Technology, writes in an accompanying perspective that "If this design can be scaled up to large network sizes, it will affect the future of computing."

That's because there are still many challenges where a neural network can easily outperform traditional computing hardware—and do so at a fraction of the energy cost. Even on a 30 nm process, it would be possible to place 25 million cells in a square centimeter, with 10,000 synapses on each cell. And all that would dissipate about a Watt.

Training and operation of an integrated neuromorphic network based on metal-oxide memristors [abstract]

MIT Technology Review

takyon writes:

The Next Platform has an article about waning interest in brain-inspired neuromorphic computing post-2013 (which has not yet delivered a "revolution in computing") and some of the developments in the field since then:

There have been a couple of noteworthy investments that have fed existing research for neuromorphic architectures. The DARPA Synapse program was one such effort, which beginning in 2008, eventually yielded IBM's "True North" chip—a 4096-core device comprised of 256 programmable "neurons" that act much like synapses in the brain, resulting in a highly energy efficient architecture that while fascinating—means an entire rethink of programming approaches. Since that time, other funding from scientific sources, including the Human Brain Project, have pushed the area further, leading to the creation of the SpiNNaker neuromorphic device, although there is still a lack of a single architecture that appears best for neuromorphic computing in general.

The problem is really that there is no "general" purpose for such devices as of yet and no widely accepted device or programmatic approach. Much of this stems from the fact that many of the existing projects are built around specific goals that vary widely. For starters, there are projects around broader neuromorphic engineering that are more centered on robotics versus large-scale computing applications (and vice versa). One of several computing-oriented approaches taken by Stanford University's Neurogrid project, which was presented in hardware in 2009 and remains an ongoing research endeavor, was to simulate the human brain, thus the programming approach and hardware design are both thus modeled as closely to the brain as possible while others are more oriented toward solving computer science related challenges related to power consumption and computational capability using the same concepts, including a 2011 effort at MIT, work at HP with memristors as a key to neuromorphic device creation, and various other smaller projects, including one spin-off of the True North architecture we described here.

[more]

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