Analog VLSI and neural systems
Analog VLSI and neural systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Nanoelectronic circuits and systems
Variability and energy awareness: a microarchitecture-level perspective
Proceedings of the 42nd annual Design Automation Conference
Applying Spiking Neural Nets to Noise Shaping
IEICE - Transactions on Information and Systems
An Inhibitory Neural-Network Circuit Exhibiting Noise Shaping with Subthreshold MOS Neuron Circuits
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A subthreshold MOS neuron circuit based on the Volterra system
IEEE Transactions on Neural Networks
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We propose a bio-inspired circuit performing pulse-density modulation with single-electron devices. The proposed circuit consists of three single-electron neuronal units, receiving the same input and are connected to a common output. The output is inhibitorily fedback to the three neuronal circuits through a capacitive coupling, tuned to obtain a winners-shareall network operation. The circuit performance was evaluated through Monte-Carlo based computer simulations. We demonstrated that the proposed circuit possesses noise-shaping characteristics, where signal and noises are separated into low and high frequency bands respectively. This significantly improved the signal-to-noise ratio (SNR) by 4.34 dB in the coupled network, as compared to the uncoupled one. The noise-shaping properties are as a result of i) the inhibitory feedback between the output and the neuronal circuits, and ii) static noises (originating from device fabrication mismatches) and dynamic noises (as a result of thermally induced random tunneling events) introduced into the network.