Artificial Intelligence Goes Physical
Copyright policy )Artificial Intelligence Goes Physical
Exploiting the intrinsic nonlinearity in physical reservoirs, e.g., dopant‐atom networks, provides a new approach toward highly efficient computing such as feature projection and classification. In a recent study by Chen et al., the computational capability of dopant‐atom network was investigated and found to diminish as the signal‐to‐noise ratio (SNR) increased, indicating the existence of an optimal bias condition. Although high SNR is often pursued in signal processing, it shows that embracing noise in non‐conventional computing systems may lead to a leap in computing capacity. This work showcased that material or device physics in different domains offer valuable substrates for complex computing functions and high energy efficiency.
- Hebei University China (People's Republic of)
- PEKING UNIVERSITY China (People's Republic of)
- Peking University China (People's Republic of)
- Peking University China (People's Republic of)
- Peking University China (People's Republic of)
Signal processing, Parallel computing, noise, Artificial intelligence, Photonic Reservoir Computing for Neural Computation, Memristive Devices for Neuromorphic Computing, Cognitive Neuroscience, Optoelectronic Reservoir Computing, Noise (video), Quantum mechanics, Engineering, Artificial Intelligence, Physical system, Computer engineering, FOS: Electrical engineering, electronic engineering, information engineering, Doping, Image (mathematics), Dopant, Atom (system on chip), Electrical and Electronic Engineering, Work (physics), Optoelectronics, Brain-inspired Computing, Materials of engineering and construction. Mechanics of materials, Neuronal Oscillations in Cortical Networks, Photonic Reservoir Computing, Neuromorphic Computing, Physics, nonlinearity, Life Sciences, Computer hardware, reservoir computing, artificial intelligence, Digital signal processing, Computer science, Materials science, Programming language, Nonlinear Dynamics, SIGNAL (programming language), Physical Sciences, Computer Science, TA401-492, Nonlinear system, Energy (signal processing), Comments, Neuroscience
Signal processing, Parallel computing, noise, Artificial intelligence, Photonic Reservoir Computing for Neural Computation, Memristive Devices for Neuromorphic Computing, Cognitive Neuroscience, Optoelectronic Reservoir Computing, Noise (video), Quantum mechanics, Engineering, Artificial Intelligence, Physical system, Computer engineering, FOS: Electrical engineering, electronic engineering, information engineering, Doping, Image (mathematics), Dopant, Atom (system on chip), Electrical and Electronic Engineering, Work (physics), Optoelectronics, Brain-inspired Computing, Materials of engineering and construction. Mechanics of materials, Neuronal Oscillations in Cortical Networks, Photonic Reservoir Computing, Neuromorphic Computing, Physics, nonlinearity, Life Sciences, Computer hardware, reservoir computing, artificial intelligence, Digital signal processing, Computer science, Materials science, Programming language, Nonlinear Dynamics, SIGNAL (programming language), Physical Sciences, Computer Science, TA401-492, Nonlinear system, Energy (signal processing), Comments, Neuroscience
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