This breakthrough, published in a recent paper in Nature Electronics, signifies a significant stride towards achieving high-performance AI computing while substantially conserving energy.

The traditional approach of executing deep neural networks on conventional digital computing architectures poses limitations in terms of performance and energy efficiency. These digital systems entail constant data transfer between memory and processing units, slowing down computations and reducing energy optimisation.

To tackle these challenges, IBM Research has harnessed the principles of analog AI, which emulates the way neural networks function in biological brains. This approach involves storing synaptic weights using nanoscale resistive memory devices, specifically Phase-change memory (PCM).

PCM devices alter their conductance through electrical pulses, enabling a continuum of values for synaptic weights. This analog method mitigates the need for excessive data transfer, as computations are executed directly in the memory—resulting in enhanced efficiency.

The newly introduced chip is a cutting-edge analog AI solution composed of 64 analog in-memory compute cores.

Each core integrates a crossbar array of synaptic unit cells alongside compact analog-to-digital converters, seamlessly transitioning between analog and digital domains. Furthermore, digital processing units within each core manage nonlinear neuronal activation functions and scaling operations. The chip also boasts a global digital processing unit and digital communication pathways for interconnectivity.

The research team demonstrated the chip’s prowess by achieving an accuracy of 92.81 percent on the CIFAR-10 image dataset—an unprecedented level of precision for analog AI chips.

The throughput per area, measured in Giga-operations per second (GOPS) by area, underscored its superior compute efficiency compared to previous in-memory computing chips. This innovative chip’s energy-efficient design coupled with its enhanced performance makes it a milestone achievement in the field of AI hardware.

The analog AI chip’s unique architecture and impressive capabilities lay the foundation for a future where energy-efficient AI computation is accessible across a diverse range of applications.

IBM Research’s breakthrough marks a pivotal moment that will help to catalyse advancements in AI-powered technologies for years to come.

(Image Credit: IBM Research)

See also: Azure and NVIDIA deliver next-gen GPU acceleration for AI

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The post IBM Research unveils breakthrough analog AI chip for efficient deep learning appeared first on AI News.

Good sleep is vital for our mental and physical wellbeing. Diagnosing problems today, however, can be difficult as it requires patients to be fitted with electrodes and various sensors.

The researchers from MIT and Massachusetts General Hospital used an AI algorithm to analyse radio signals around a subject. These readings are translated into the stages of sleep: awake, light, deep, or rapid eye movement.

“Imagine if your WiFi router knows when you are dreaming and can monitor whether you are having enough deep sleep, which is necessary for memory consolidation,” says Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, who led the study. “Our vision is developing health sensors that will disappear into the background and capture physiological signals and important health metrics, without asking the user to change her behavior in any way.”

There are possible applications for utilising this data beyond healthcare. For example, all the lights in a home could switch off automatically when occupants fall asleep to conserve energy. If occupants wake in the night to use the bathroom, certain lights could be switched on to guide the way.

With more than 50 million current (known) sufferers of sleep disorders in America alone, this research could be groundbreaking. It will help to diagnose and monitor problems without cumbersome and expensive specialist equipment.

“The opportunity is very big because we don’t understand sleep well, and a high fraction of the population has sleep problems,” says Mingmin Zhao, an MIT graduate and the paper’s first author. “We have this technology that, if we can make it work, can move us from a world where we do sleep studies once every few months in the sleep lab to continuous sleep studies in the home.”

Katabi and Zhao worked on the study with Matt Bianchi, chief of the division of sleep medicine at MGH, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science and a member of the Institute for Data, Systems, and Society at MIT, and Shichao Yue, another MIT graduate student who is also a co-author on the paper.

AI beyond sleep disorders

Some of Katabi’s previous work alongside her fellow researchers at MIT also made use of radio waves. One laptop-sized box, which emits low-power RF signals, revealed vital signs including pulse and breathing rate. This could be used to monitor the elderly to alert medical professionals of worrying changes to their vitals.

Artificial intelligence using deep neural networks has made all of this possible. Extracting relevant information from the large datasets while removing erroneous results required the researchers to build their own algorithm.

“Our device allows you not only to remove all of these sensors that you put on the person and make it a much better experience that can be done at home, it also makes the job of the doctor and the sleep technologist much easier,” Katabi says. “They don’t have to go through the data and manually label it.”

The researchers will present their new sensor at the International Conference on Machine Learning on August 9th, 2017.

What health applications are you excited to see AI used for? Let us know in the comments.

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