via University of Texas at Austin
The rapid progression of technology has led to a huge increase in energy usage to process the massive troves of data generated by devices. But researchers in 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.
“Right now, the methods for training your neural networks are very energy-intensive,” said Jean Anne Incorvia, an assistant professor in the Cockrell School’s Department of Electrical and Computer Engineering. “What our work can do is help reduce the training effort and energy costs.”
The researchers’ findings were published this week in IOP Nanotechnology. Incorvia led the study with first author and second-year graduate student Can Cui. Incorvia and Cui discovered that spacing magnetic nanowires, acting as artificial neurons, in certain ways naturally increases the ability for the artificial neurons to compete against each other, with the most activated ones winning out. Achieving this effect, known as “lateral inhibition,” traditionally requires extra circuitry within computers, which increases costs and takes more energy and space.
Incorvia said their method provides an energy reduction of 20 to 30 times the amount used by a standard back-propagation algorithm when performing the same learning tasks.
The same way human brains contain neurons, new-era computers have artificial versions of these integral nerve cells. Lateral inhibition occurs when the neurons firing the fastest are able to prevent slower neurons from firing. In computing, this cuts down on energy use in processing data.
Incorvia explains that the way computers operate is fundamentally changing. A major trend is the concept of neuromorphic computing, which is essentially designing computers to think like human brains. Instead of processing tasks one at a time, these smarter devices are meant to analyze huge amounts of data simultaneously. These innovations have powered the revolution in machine learning and artificial intelligence that has dominated the technology landscape in recent years.
This research focused on interactions between two magnetic neurons and initial results on interactions of multiple neurons. The next step involves applying the findings to larger sets of multiple neurons as well as experimental verification of their findings.
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
- Quantum treatment sheds fresh light on triboelectricityon October 6, 2020 at 7:37 am
New mathematical formulation describes “charging by rubbing” in terms of changing populations of electron states, showing how energy is converted from motion to electricity ...
- Magnetostrictive, Piezoelectric Effects Partner to Power Implanted Neural Stimulatoron October 5, 2020 at 1:46 pm
To wirelessly power an implanted neural stimulator, a research team combined materials to synergistically use the magnetorestrictive and piezoelectric effects.
- 15 Best Indoor Cycling Bikes (Peloton Alternatives)on October 4, 2020 at 11:47 pm
We've compared and reviewed the best indoor cycling bikes for every budget. Discover our top picks for every type of cyclist.
- Fed. Circ. Patent Indefiniteness Ruling Bears Drafting Lessonson October 2, 2020 at 1:07 pm
The recent Federal Circuit decision IQASR v. Wendt, finding a patent for sorting materials from old cars invalid as indefinite, illustrates that patent drafters need to ensure that a patent ...
- Eriez touts evolution of P-Rex drum magneton October 1, 2020 at 9:57 pm
Company says its drum magnet offers boosted recovery rate in shredder yards and in other applications, including waste-to-energy.
Go deeper with Google Headlines on:
Go deeper with Bing News on:
- Sandia labs and Intel partner to develop computing technologyon October 5, 2020 at 4:20 am
Computer chip manufacturer Intel announced a new partnership with Sandia National Laboratories on Friday — one that could spur the development of new computing technology. The three-year agreement ...
- Neuromorphic computing could solve the tech industry's looming crisison October 5, 2020 at 3:15 am
Brain-based computing could help tech companies overcome the current constraints of chip design. What's the best computer in the world? The most souped-up, high-end gaming rig? Whatever supercomputer ...
- Sandia, Intel team up on next-gen computingon October 2, 2020 at 2:37 pm
Sandia National Laboratories and Intel Corp. are teaming up to build next-generation supercomputers that could function like human brains, instantaneously resolving complex problems with a fraction of ...
- Intel and Sandia National Labs Collaborate on Neuromorphic Computingon October 2, 2020 at 9:16 am
Intel Federal has announced an agreement with Sandia Laboratories to explore the use of neuromorphic computing for scaled-up computational problems ...
- Intel and Sandia National Labs Collaborate on Neuromorphic Computingon October 2, 2020 at 7:31 am
“By applying the high-speed, high-efficiency and adaptive capabilities of neuromorphic computing architecture, Sandia National Labs will explore the acceleration of high-demand and frequently ...