Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.
For numerous Radio-Frequency applications such as medicine, RF fingerprinting or radar classification, it is important to be able to apply Artificial Neural Network on RF signals. In this work we show that it is possible to apply directly Multiply-And-Accumulate operations on RF signals without digitalization, thanks to Magnetic Tunnel Junctions (MTJs). These devices are similar to the magnetic memories already industrialized and compatible with CMOS.
We show experimentally that a chain of these MTJs can rectify simultaneously different RF signals, and that the synaptic weight encoded by each junction can be tune with their resonance frequency.
Through simulations we train a layer of these junctions to solve a handwritten digit dataset. Finally, we show that our system can scale to multi-layer neural networks using MTJs to emulate neurons.
Our proposition is a fast and compact system that allows to receive and process RF signals in situ and at the nanoscale.
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