New Focus Issue on Memristive and Memcapacitive Materials, Devices, and Circuits for Energy-efficient AI Computing
Guest-edited by Prof. Laura Bégon-Lours, the upcoming focus issue in Neuromorphic Computing and Engineering will explore cutting-edge advancements in memristive and memcapacitive technologies. Submissions are now invited.
A new focus issue of Neuromorphic Computing and Engineering explores breakthroughs in memristive and memcapacitive materials, devices, and circuits for energy-efficient AI computing. These innovations have the potential to revolutionize edge AI systems, IoT devices, and neuromorphic computing by enabling AI in power-constrained environments.
Guest edited by Prof. Laura Bégon-Lours from ETH Zürich, this collection addresses the growing need for energy-efficient computing solutions, particularly in AI systems that face challenges like the "memory wall" problem. Topics include memristive and memcapacitive materials (composition tuning, interface engineering), device engineering (improving uniformity and reliability), circuit design (peripheral circuitry, signal conversion) and hybrid approaches combining new memory tech with traditional CMOS.
Contributions from researchers worldwide are encouraged. For more details, click on the link below.