Affiliation:
1. National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine the First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi 710049 China
2. Department of hepatobiliary surgery the First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi 710049 China
3. Frontier Institute of Science and Technology (FIST) Xi'an Jiaotong University Xi'an Shaanxi 710049 China
4. Micro‐and Nano‐technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an Shaanxi 710049 China
5. College of Artificial Intelligence Brain‐inspired Computing & Intelligent Control of Chongqing Key Lab Southwest University Chongqing 400715 China
6. School of Mechanical Engineering Shanghai Jiao Tong University 800 DongChuan Rd Shanghai 200240 China
Abstract
AbstractIn the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain‐like chips, which are known for their robust processing power and energy‐efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor‐based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse‐neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.
Funder
Xi’an Jiaotong University
National Natural Science Foundation of China
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science