Excitability in autonomous Boolean networks

We demonstrate theoretically and experimentally that excitable systems can be built with autonomous Boolean networks. Their experimental implementation is realized with asynchronous logic gates on a reconfigurabe chip. When these excitable systems are assembled into time-delay networks, their dynamics display nanosecond time-scale spike synchronization patterns that are controllable in period and phase.Comment: 6 pages, 5 figures, accepted in Europhysics Letters (epljournal.edpsciences.org

Single-Input Signature Register-Based Time Delay Reservoir

Machine learning continues to play a critical role in our society. The ability to automatically identify intricate relationships in large volumes of data has proven incredibly useful for problems such as automatic speech recognition and image processing. In particular, neural networks have become increasingly popular in a wide set of application domains, given their ability to solve complex problems and process high-dimensional data. However, the impressive performance of state-of-the-art neural networks comes at the cost of large area and power consumption for the computation resources used in training and inference. As a result, a growing area of research concerns hardware implementations of neural networks. This work proposes a hardware-friendly design for a time-delay reservoir (TDR), a type of recurrent neural network. TDRs represent one class of reservoir computing neural network topologies, which employ random spatio-temporal feature extraction from time series data in order to produce a linearly separable set of features. Reservoir computing topologies differ from traditional recurrent neural networks because their recurrent weights are fixed, and the only the feedforward output weights need to be trained, usually with linear regression. Previous work on TDRs includes photonic implementation, software implementation, and both digital and analog electronic implementations. This work adds to the body of previous research by exploring the design space of a novel TDR based on single-input signature registers (SISRs), which are common digital circuits used for built-in self-test. The work is motivated by the structural similarity (delayed feedback loop) between TDRs and SISRs, and the possibility of dual-purpose of SISRs for conventional testing as well as machine learning within a single chip. The proposed designs can perform classification on multivariate datasets and perform better than a traditional TDR with quantized reservoir states for parity check, MNIST classification, and temperature prediction tasks. Classification accuracies of up to 100% were observed for some configurations of the SISR for the parity check task and accuracies of up to 85% were observed for MNIST classification. We also observe overfitting on a temperature prediction task with longer data sequences and provide analyses of the results based on the reservoir dynamics, as measured by the rate of divergence between SISR states and the SISR period

Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing

Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its nonlinear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work indicates that realizing RNN architectures using custom integrated circuits and reconfigurable hardware platforms yields significant improvements in power and latency. In this research, we propose a neuromemristive RC architecture, with doubly twisted toroidal structure, that is validated for biosignal processing applications. We exploit the device mismatch to implement the random weight distributions within the reservoir and propose mixed-signal subthreshold circuits for energy efficiency. A comprehensive analysis is performed to compare the efficiency of the neuromemristive RC architecture in both digital(reconfigurable) and subthreshold mixed-signal realizations. Both EEG and EMG biosignal benchmarks are used for validating the RC designs. The proposed RC architecture demonstrated an accuracy of 90% and 84% for epileptic seizure detection and EMG prosthetic finger control respectively