Memristors have attracted interest for mimicking synapses in more energy-efficient scalable approaches to "brain-like" neuromorphic computing. However, their intrinsic variability has inhibited the performance of memristor-based neural networks, stymying progress. Now researchers in Beijing have shown that by introducing "fuzziness" into their neural network
CS Digest Section: Neuromorphic Computing
Sophisticated cybersecurity systems excel at finding "bad apples" in computer networks, but they lack the computing power to identify the threats directly.
While the steady tick-tock of the tried and true is still audible, the last two years have ushered a fresh wave of new architectures targeting deep learning and other specialized workloads, as well as a bevy of forthcoming hybrids with FPGAs, zippier GPUs, and swiftly emerging open architectures.
The Air Force Research Lab (AFRL) reports good results from using a “neuromorphic” chip made by IBM to identify military and civilian vehicles in radar-generated aerial imagery.
As the last decade ended, ARM’s CTO Mike Muller warned the era of dark silicon was approaching.
We have heard about a great number of new architectures and approaches to scalable and efficient deep learning processing that sit outside of the standard CPU, GPU, and FPGA box and while each is different, many are leveraging a common element at all-important memory layer.
The National Science Foundation (NSF) has awarded 18 grants to multidisciplinary teams from across the United States to conduct frontier research focused on neural and cognitive systems. Each award provides a research team with up to $1 million over two to four years.
Brain2Grid SCADA system being "grown" by Clemson and Georgia Tech -"Neurocontrol offers nonlinear adaptive optimal control"
$110M effort by NIH, NSF and DARPA in 2014 for a "new generation of information processing systems"
Chip-architecture breakthrough accelerates path to exascale computing; Helps computers tackle complex, cognitive tasks such as pattern recognition and sensory processing.