Exploring the Opportunity of Implementing Neuromorphic Computing Systems with Spintronic Devices
Bonan Yan1,a, Fan Chen1,b, Yaojun Zhangy2, Chang Song1,c, Hai Li1,d, Yiran Chen1,e
1Duke University
abonan.yan@duke.edu
bfan.chen@duke.edu
cchang.song@duke.edu
dhai.li@duke.edu
eyiran.chen@duke.edu
2Duke University
yaz24@pitt.edu
ABSTRACT
Many cognitive algorithms such as neural networks cannot be efficiently executed by von Neumann architectures, the performance of which is constrained by the memory wall between microprocessor and memory hierarchy. Hence, researchers started to investigate new computing paradigms such as neuromorphic computing that can adapt their structure to the topology of the algorithms and accelerate their executions. New computing units have been also invented to support this effort by leveraging emerging nano-devices. In this work, we will discuss the opportunity of implementing neuromorphic computing systems with spintronic devices. We will also provide insights on how spintronic devices fit into different part of neuromorphic computing systems. Approaches to optimize the circuits are also discussed.
