Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing. (14th April 2020)

• Record Type: Journal Article
• Authors: Rahimi Azghadi, Mostafa; Chen, Ying-Chen; Eshraghian, Jason K.; Chen, Jia; Lin, Chih-Yang; Amirsoleimani, Amirali; Mehonic, Adnan; Kenyon, Anthony J.; Fowler, Burt; Lee, Jack C.; Chang, Yao-Feng
• Is Part Of: Advanced intelligent systems. Volume 2:Number 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-14
• Subjects: complementary metal-oxide semiconductors -- memristors -- neuromorphic computing -- resistive random access memory -- unconventional computing; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.201900189
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed, and noise‐tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well‐established complementary metal‐oxide semiconductor (CMOS), and emerging memristive technologies such as SiO x ‐based memristors. Herein, recent progress in CMOS, SiO x ‐based memristive, and mixed CMOS‐memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate‐based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies. Abstract : Herein, recent advances in CMOS, SiO x ‐based memristive, and mixed CMOS–memristive hardware for neuromorphic systems are discussed, along with the challenges and opportunities. New and published results are provided from various devices that are developed to replicate selected functions of neurons, synapses, and simple spiking networks, which are used for MNIST and pattern classifications.