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., and Chang, Yao-Feng (2020) Complementary metal‐oxide semiconductor and memristive hardware for neuromorphic computing. Advanced Intelligent Systems, 2 (5). 1900189.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution. Download (7MB) | Preview

 

73

601

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 SiOx‐based memristors. Herein, recent progress in CMOS, SiOx‐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.

Actions (Repository Staff Only)

Item Control Page