DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.015
Publication date: 9th January 2023
In-memory computing is emerging as a promising new platform for implementing machine learning, data analytics and artificial intelligence inspired by the human brain. Emerging resistive/memristive memory devices can enable brain-inspired computing primitives, thanks to their tunable volatile/nonvolatile characteristics, their excellent scaling in both 2D and 3D, their ability to run in-memory compute algorithms, and their unique physical properties that can mimic the individual building blocks in the brain, such as neurons, synapses and dendrites. This talk will present the status and challenges about resistive switching devices for in-memory computing. A broad scope of emerging devices will be illustrated, ranging from resistive switching memory (RRAM) and memtransistors based on 2D semiconductors such as MoS2. The prospects for hybrid memristive-CMOS circuits for neuromorphic computing will be discussed in terms of scaling and energy efficiency.
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International Conference on Hybrid and Organic Photovoltaics
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