Collaborating Authors

Neuromorphic computing with multi-memristive synapses


The human brain with less than 20 W of power consumption offers a processing capability that exceeds the petaflops mark, and thus outperforms state-of-the-art supercomputers by several orders of magnitude in terms of energy efficiency and volume. Building ultra-low-power cognitive computing systems inspired by the operating principles of the brain is a promising avenue towards achieving such efficiency. Recently, deep learning has revolutionized the field of machine learning by providing human-like performance in areas, such as computer vision, speech recognition, and complex strategic games1. However, current hardware implementations of deep neural networks are still far from competing with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption. One of the reasons for this inefficiency is that most neural networks are implemented on computing systems based on the conventional von Neumann architecture with separate memory and processing units.

Novel synaptic architecture for brain inspired computing


The findings are an important step toward building more energy-efficient computing systems that also are capable of learning and adaptation in the real world. They were published last week in a paper in the journal Nature Communications. The researchers, Bipin Rajendran, an associate professor of electrical and computer engineering, and S. R. Nandakumar, a graduate student in electrical engineering, have been developing brain-inspired computing systems that could be used for a wide range of big data applications. Over the past few years, deep learning algorithms have proven to be highly successful in solving complex cognitive tasks such as controlling self-driving cars and language understanding. At the heart of these algorithms are artificial neural networks -- mathematical models of the neurons and synapses of the brain -- that are fed huge amounts of data so that the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.

Synaptic Architecture for Brain Inspired Computing: IBM Research


Our brain and all its magnificent capabilities is powered by less than 20 watts. Stop to think about that for a second. As I write this blog my laptop is using about 80 watts, yet at only a fourth of the power, our brain outperforms state-of-the-art supercomputers by several orders of magnitude when it comes to energy efficiency and volume. For this reason it shouldn't be surprising that scientists around the world are seeking inspiration from the human brain as a promising avenue towards the development of next generation AI computing systems and while the IT industry has made significant progress in the past several years, particularly in using machine learning for computer vision and speech recognition, current technology is hitting a wall when it comes to deep neural networks matching the power efficiency of their biological counterpart, but this could be about to change. As reported last week in Nature Communications, my colleagues and I at IBM Research and collaborators at EPFL and the New Jersey Institute of Technology have developed and experimentally tested an artificial synapse architecture using 1 million devices -- a significant step towards realizing large-scale and energy efficient neuromorphic computing technology.

Bit-Serial Neural Networks

Neural Information Processing Systems

This arises from representation which gives rise to gentle degradation as faults appear. These functions are attractive to implementation in VLSI and WSI. For example, the natural be useful in silicon wafers with imperfect yield, where thefault - tolerance could is approximately proportional to the non-functioning siliconnetwork degradation area. To cast neural networks in engineering language, a neuron is a state machine that is either "on" or "off', which in general assumes intermediate states as it switches The synapses weighting the signals from asmoothly between these extrema.

New AI Paradigm May Reduce a Heavy Carbon Footprint


Artificial intelligence (AI) machine learning can have a considerable carbon footprint. Deep learning is inherently costly, as it requires massive computational and energy resources. Now researchers in the U.K. have discovered how to create an energy-efficient artificial neural network without sacrificing accuracy and published the findings in Nature Communications on August 26, 2020. The biological brain is the inspiration for neuromorphic computing--an interdisciplinary approach that draws upon neuroscience, physics, artificial intelligence, computer science, and electrical engineering to create artificial neural systems that mimic biological functions and systems. The human brain is a complex system of roughly 86 billion neurons, 200 billion neurons, and hundreds of trillions of synapses.