The Future of AI: Explore Neuromorphic Computing Research

Dive into a curated collection of papers on AI hardware, memristors, and brain-inspired computing View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS ABOUT THE JOURNAL APL Machine Learning Accelerating AI With Brain-Inspired Hardware Neuromorphic computing offers a revolutionary path forward. APL Machine Learning (AML) is at the forefront of this exciting new field, publishing cutting-edge research that is shaping the future of AI. We have curated a research collection exploring the latest breakthroughs in brain-inspired hardware and devices.  Explore how researchers are training self-learning circuits and developing biologically plausible algorithms for applications like seizure detection. Discover new insights into AI-driven models for pulse programming of memristive devices and scaling algorithms for hardware-compatible learning.  This article selection offers a comprehensive look at the advancements defining the next generation of computing. We also invite you to consider AML as the home for your own high-impact research in this rapidly evolving field. PUBLISH YOUR PAPER WITH AML > We now invite you to read, cite, and share these exceptional papers with your peers. Training self-learning circuits for power-efficient solutions Menachem Stern, Sam Dillavou, Dinesh Jayaraman, Douglas J. Durian, et al. READ MORE > Brain-inspired learning in artificial neural networks: A review Samuel Schmidgall, Rojin Ziaei, Jascha Achterberg, Louis Kirsch, et al. READ MORE > Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge Luis Fernando Herbozo Contreras, Zhaojing Huang, Leping Yu, Armin Nikpour, et al. READ MORE > AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing Jiaxiang Chen, Haitao Du, Haolan Qu, Han Gao, et al. READ MORE > AI-driven model for optimized pulse programming of memristive devices Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, et al. READ MORE > Can ferroelectric tunnel junction be a game changer as eNVM and in neuromorphic hardware? Sayani Majumdar READ MORE > Stochastic compact model for memory and threshold switching memristors Jordi Suñé, Enrique Miranda READ MORE > A memristive computational neural network model for time-series processing Veronica Pistolesi, Andrea Ceni, Gianluca Milano, Carlo Ricciardi, et al. READ MORE > Scaling of hardware-compatible perturbative training algorithms B. G. Oripov, A. Dienstfrey, A. N. McCaughan, S. M. Buckley READ MORE > Hard way or hardware? Taking the heat out of AI Ampattu R. Jayakrishnan, Markus Hellenbrand, Sebastian Dixon, Adnan Mehonic, et al. READ MORE > FURTHER NEUROMORPHIC RESEARCH Explore more novel research from other journals in our Machine Learning portfolio. Ferroelectric artificial synapses for high-performance neuromorphic computing: Status, prospects, and challenges Le Zhao, Hong Fang, Jie Wang, Fang Nie, et al. Appl. Phys. Lett. READ MORE > Enhancing plasticity in optoelectronic artificial synapses: A pathway to efficient neuromorphic computing Jiahao Yuan, Chao Wu, Shunli Wang, Fengmin Wu, et al. Appl. Phys. Lett. READ MORE > A review on device requirements of resistive random access memory (RRAM)-based neuromorphic computing Jeong Hyun Yoon, Young-Woong Song, Wooho Ham, Jeong-Min Park, et al. APL Mater. READ MORE > Roadmap to neuromorphic computing with emerging technologies Adnan Mehonic, Daniele Ielmini, Kaushik Roy, Onur Mutlu, et al. APL Mater. READ MORE > Curious what happens after you hit submit? 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