We investigate VLSI systems using biological computational principles. The elegance of biological systems throughout the structure levels provides possible solutions to many engineering challenges. Specifically, we investigate neural systems at the synaptic level and at the sensorimotor integration level, which inspire our similar implementations in silicon. For both VLSI systems, we use floating gate MOSFETs in standard CMOS processes as nonvolatile storage elements, which enable adaptation and programmability.
We propose a compact silicon stochastic synapse and methods to incorporate activity-dependent dynamics, which emulate a biological stochastic synapse. We implement and demonstrate the first silicon stochastic synapse with short-term depression by modulating the influence of noise on the circuit. The circuit exhibits true randomness and similar behavior of rate normalization and information redundancy reduction as its biological counterparts. The circuit behavior also agrees well with the theory and simulation of a circuit model based on a subtractive single release model.
To understand the stochastic behavior of the silicon stochastic synapse and the stochastic operation of conventional circuits due to semiconductor technology scaling, we develop the stochastic modeling of circuits and transient analysis from the numerical solution of the stochastic model. The analytical solution of steady state distribution could be obtained from first principles. Small signal stochastic models show the interaction between noise and circuit dynamics, elucidating the effect of device parameters and biases on the stochastic behavior.
We investigate optic flow wide field integration based navigation inspired from the fly in simulation, theory, and VLSI design. We generalize the framework to limited view angles. We design and test an integrated motion image sensor with on-chip optic flow estimation, adaptation, and programmable spatial filtering to directly interface with actuators for autonomous navigation. This is the first reported image sensor that uses the spatial motion pattern to extract motion parameters enabled by the mismatch compensation and programmable filters. The sensor is integrated with a ground vehicle and navigation through simple tunnel environments is demonstrated. It provides light weight and low power integrated approach to autonomous navigation of micro air vehicles.
The mismatch compensation and programmable filters can also be applied to other sensory front-end where on-chip spatial processing is required and distortion from fabrication mismatch among sensor units has to be reduced. The sensor is integrated with a ground vehicle and navigation through simple tunnel environments is demonstrated with limited information from only one horizontal line of optical input of height 2:4± and field of view angle 83:1±.
Source: University of Maryland
Author: Peng Xu