Physics for neuromorphic computing
By Danijela Marković, Alice Mizrahi, Damien Querlioz, Julie Grollier. Nature Reviews Physics, 2020
1,039 citations recorded by the source corpus.
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. Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Including more physics in the algorithms and nanoscale materials used for computing could have a major impact in this field.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/6dc50a0e83b090504870311b0d8008b87874ab75
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Citations in the corpus
- Deep physical neural networks trained with backpropagation (2021, methodology): “8–10), we shaped pulses from a mode-locked titanium:sapphire laser (Spectra Physics Tsunami, centred around 780 nm and pulse duration around 100 fs) using a custom pulse shaper.”