A study on resistive-type truncated CVNS Distributed Neural Networks
Golnar Khodabandehloo, Mitra Mirhassani, Majid Ahmadi · 2011
Distributed Neural Networks (DNNs) are generally providing self-scaling property together with higher noise immunity for resistive-type neural networks. Continuous Valued Number System (CVNS) is a potential candidate to build the DNNs; however, implementation of a CVNS digit in its complete form needs a high resolution environment which is not practical. Truncation methods are applied to CVNS digits to make them adaptable to the low resolution environments. However, truncated CVNS operations may decrease the accuracy and immunity to noise compared to the complete CVNS operations. In this work, a truncated CVNS DNN is proposed, and studies over Noise to Signal Ratio (NSR) and accuracy are provided. Studies show that the accuracy is acceptable, and the NSR is still less than the NSR of conventional DNNs.