Reservoir Computing with Boolean Logic Network Circuits
Reservoir Computing with Boolean Logic Network Circuits
Files
Publication or External Link
Date
2021
Authors
Komkov, Heidi
Advisor
Lathrop, Daniel P
Citation
DRUM DOI
Abstract
To push the frontiers of machine learning, completely new computing architectures must be explored which efficiently use hardware resources. We test an unconventional use of digital logic gate circuits for reservoir computing, a machine learning algorithm that is used for rapid time series processing. In our approach, logic gates are configured into networks that can exhibit complex dynamics. Rather than the gates explicitly computing pre-programmed instructions, they are used collectively as a dynamical system that transforms input data into a higher dimensional representation. We probe the dynamics of such circuits using discrete components on a circuit board as well as an FPGA implementation. We show favorable machine learning performance, including radiofrequency classification accuracy comparableto a state of the art convolutional neural network with a fraction of the trainable parameters. Finally, we discuss the design and fabrication of a reservoir computing ASIC for high-speed time series processing.