Sign up for our newsletter and get the latest HPC news and analysis.
Send me information from insideHPC:


New Memristors at MIT: Networks of Artificial Brain Synapses for Neuromorphic Devices

A possible glimpse at a future form of high performance edge computing – networks of artificial brain synapses – developed by engineers at the Massachusetts Institute of Technology is showing promise as a new memristor design for neuromorphic devices, which mimic the neural architecture in the human brain. Published today in Nature Nanotechnology, results of […]

Using Magnetic Circuits for Energy Efficient Big Data Processing

Researchers at the Cockrell School of Engineering at The University of Texas at Austin have found a way to make the new generation of smart computers more energy efficient. “Traditionally, silicon chips have formed the building blocks of the infrastructure that powers computers. But this research uses magnetic components instead of silicon and discovers new information about how the physics of the magnetic components can cut energy costs and requirements of training algorithms — neural networks that can think like humans and do things like recognize images and patterns.”

Intel Scales Neuromorphic System to 100 Million Neurons

Today Intel unveiled Pohoiki Springs, its latest and most powerful neuromorphic research system providing the computational capacity of 100 million neurons. “Pohoiki Springs scales up our Loihi neuromorphic research chip by more than 750 times, while operating at a power level of under 500 watts,” said Mike Davies, director of Intel’s Neuromorphic Computing Lab. “The system enables our research partners to explore ways to accelerate workloads that run slowly today on conventional architectures, including high-performance computing (HPC) systems.”

Intel’s Neuromorphic Chip Can Sniff Out Hazardous Chemicals

Researchers have demonstrated how neuromorphic chips can mimic olfactory senses for use in industrial and medical applications. “In a joint paper published in Nature Machine Intelligence, researchers from Intel Labs and Cornell University demonstrated the ability of Intel’s neuromorphic research chip, Loihi, to learn and recognize hazardous chemicals in the presence of significant noise and occlusion. Loihi learned each odor with just a single sample without disrupting its memory of the previously learned scents. It demonstrated superior recognition accuracy compared to conventional state-of-the-art methods, including a deep learning solution that required 3000x more training samples per class to reach the same level of classification accuracy.”

Podcast: The Evolution of Neuromorphic Computing

Intel’s Mike Davies describes Intel’s Loihi, a neuromorphic research chip that contains over 130,000 “neurons.” “To be sure, neuromorphic computing isn’t biomimicry or about reconstructing the brain in silicon. Rather, it’s about understanding the processes and structures of neuroscience and using those insights to inform research, engineering, and technology.”

Intel Labs Unveils Pohoiki Beach 64-Chip Neuromorphic System

At the DARPA ERI summit this week, Intel Labs director Rich Uhlig unveiled “Pohoiki Beach” – a 64-Loihi Chip Neuromorphic system capable of simulating eight million neurons. Now available to the broader research community, the Pohoiki Beach enables researchers to experiment with Intel’s brain-inspired research chip, Loihi, which applies the principles found in the biological brains to computer architectures. 

Sandia Powers Breakthroughs in Neuromorphic Computing

Researchers at Sandia National Laboratories have collaborated with Stanford University and University of Massachusetts, Amherst to address the challenges of neuromorphic computing, which mimics the way the human brain carries out data-centric tasks. The work has lead to recent breakthroughs in neuromorphic computing and the broader fields of organic electronics and solid-state electrochemistry.

SC18 Preview: Steve Furber on Brain-Inspired Massively-Parallel Computing

SC18 continues its series of Invited Talk previews with this quick look at “Brain-Inspired Massively-Parallel Computing” by Stephen Furber. “The SpiNNaker (Spiking Neural Network Architecture) platform is an example of a highly flexible digital neuromorphic platform, based upon a massively-parallel configuration of small processors with a bespoke interconnect fabric designed to support the very high connectivity of biological neural nets in real-time models. Although designed primarily to support brain science, it can also be used to explore more applications-oriented research.”

Video: Leading the Evolution of Compute with Neuromorphic and Quantum Computing

Jim Held from Intel Labs gave this talk at the Intel HPC Developer Conference in Denver. “Intel recently announced important progress in our research into future novel microarchitectures and device technology: neuromorphic and quantum computing. Loihi, our recently announced neuromorphic research chip, is extremely energy-efficient, uses data to learn and make inferences, gets smarter over time and does not need to be trained in the traditional way. Quantum computing offers the potential for exponentially greater performance on many algorithms that are computationally challenging on today’s computing architectures.”

IBM Phase-Change Device Imitates Functionality of Neurons

IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing.