Beyond-CMOS Non-Boolean Logic Benchmarking: Insights and Future Directions
Chenyun Pan and Azad Naeemi
Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
ABSTRACT
Emerging technologies are facing significant challenges to compete with CMOS with respect to Boolean logic. There is an increasing need for using non-traditional circuits to realize the full potential of beyond-CMOS devices. This paper presents a uniform benchmarking methodology for non-Boolean computation based on the cellular neural network (CNN) for a variety of beyond-CMOS device technologies, including chargebased and spintronic devices. Three types of CNN implementations are investigated benchmarked for a given input noise and recall accuracy target using analog, digital, and spintronic circuits. Results demonstrate that spintronic devices are promising candidates to implement CNNs, where up to 3x EDP improvement is predicted in domain wall devices compared to its conventional CMOS counterpart. This shows that alternative non- Boolean computing platforms are crucial for developing future emerging technologies.
Keywords: Cellular neural network, Beyond-CMOS technology, Performance benchmarking.
