This paper presents the theory behind a model for a two-stage analog network for edge detection and image reconstruction to be implemented in VLSI. Edges are detected in the first stage using the multi-scale veto rule, which eliminates candidates that do not pass a threshold test at each of a set of different spatial scales. The image is reconstructed in the second stage from the brightness values adjacent to edge locations. The MSV rule allows good localization and efficient noise removal. Since the reconstructed images are visually similar to the originals, the possibility exists of achieving significant bandwidth compression.

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The cochlea is an organ which extracts frequency information from the input sound wave. It also produces nerve signals, which are further analysed by the brain and ultimately lead to perception of the sound. An existing model of the cochlea by Fragni`ere is first analysed by simulation. This passive model is found to have the properties that the living cochlea does in terms of the frequency response. An analog VLSI circuit implementation of this cochlear model in CMOS weak inversion is proposed, using log-domain filters in current domain. It is fabricated on a chip and a measurement of a basilar membrane section is performed. The measurement shows a reasonable agreement to the model. However, the circuit is found to have a problem related to transistor mismatch, causing different behaviour in identical circuit blocks. An active cochlear model is proposed to overcome this problem. The model incorporates the effect of the outer hair cells in the living cochlea, which controls the quality factor of the basilar membrane filters. The outer hair cells are incorporated as an extra voltage source in series with the basilar membrane resonator. Its value saturates as the input signal becomes larger, making the behaviour rather closer to that of a passive model. The simulation results show this nonlinear phenomenon, which is also seen in the living cochlea. The contribution of this thesis is summarised as follows: a) the first CMOS weak inversion current domain basilar membrane resonator is designed and fabricated, and b) the first active two-dimensional cochlear model for analog VLSI implementation is developed.

For applications involving the control of moving vehicles, the recovery of relative motion between a camera and its environment is of high utility. This thesis describes the design and testing of a real-time analog VLSI chip which estimates the focus of expansion (FOE) from measured time-varying images. Our approach assumes a camera moving through a fixed world with translational velocity; the FOE is the projection of the translation vector onto the image plane. This location is the point towards which the camera is moving, and other points appear to be expanding outward from. By way of the camera imaging parameters, the location of the FOE gives the direction of 3-D translation. The algorithm we use for estimating the FOE minimizes the sum of squares of the differences at every pixel between the observed time variation of brightness and the predicted variation given the assumed position of the FOE. This minimization is not straightforward, because the relationship between the brightness derivatives depends on the unknown distance to the surface being imaged. However, image points where brightness is instantaneously constant play a critical role. Ideally, the FOE would be at the intersection of the tangents to the iso-brightness contours at these "stationary" points. In practice, brightness derivatives are hard to estimate accurately given that the image is quite noisy. Reliable results can nevertheless be obtained if the image contains many stationary points and the point is found that minimizes the sum of squares of the perpendicular distances from the tangents at the stationary points. The FOE chip calculates the gradient of this least-squares minimization sum, and the estimation is performed by closing a feedback loop around it. The chip has been implemented using an embedded CCD imager for image acquisition and a row-parallel processing scheme. A 64 x 64 version was fabricated in a 2um CCD/ BiCMOS process through MOSIS with a design goal of 200 mW of on-chip power, a top frame rate of 1000 frames/second, and a basic accuracy of 5%. A complete experimental system which estimates the FOE in real time using real motion and image scenes is demonstrated.

The cochlea is an organ which extracts frequency information from the input sound wave. It also produces nerve signals, which are further analysed by the brain and ultimately lead to perception of the sound. An existing model of the cochlea by Fragni`ere is first analysed by simulation. This passive model is found to have the properties that the living cochlea does in terms of the frequency response. An analog VLSI circuit implementation of this cochlear model in CMOS weak inversion is proposed, using log-domain filters in current domain. It is fabricated on a chip and a measurement of a basilar membrane section is performed. The measurement shows a reasonable agreement to the model. However, the circuit is found to have a problem related to transistor mismatch, causing different behaviour in identical circuit blocks. An active cochlear model is proposed to overcome this problem. The model incorporates the effect of the outer hair cells in the living cochlea, which controls the quality factor of the basilar membrane filters. The outer hair cells are incorporated as an extra voltage source in series with the basilar membrane resonator. Its value saturates as the input signal becomes larger, making the behaviour rather closer to that of a passive model. The simulation results show this nonlinear phenomenon, which is also seen in the living cochlea. The contribution of this thesis is summarised as follows: a) the first CMOS weak inversion current domain basilar membrane resonator is designed and fabricated, and b) the first active two-dimensional cochlear model for analog VLSI implementation is developed.

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No description available.

This work is a first step towards a long-term goal of understanding computations occurring in the brain and using those principles to make more efficient machines. The traditional computing paradigm calls for using digital supercomputers to simulate large scale brain-like neural networks resulting in large power consumption which limits scalability or model detail. For example, IBM's digital simulation of a cat brain with simplistic neurons and synapses consumes power equivalent to that of a thousand houses! Instead of digital methods, this work uses analog processing concepts to develop scalable, low-power silicon models of neurons which have been shown to be around ten thousand times more power efficient. This has been achieved by modeling the dynamical behavior of Hodgkin-Huxley (H-H) or Morris-Lecar type equations instead of modeling the exact equations themselves. In particular, the two silicon neuron designs described exhibit a Hopf and a saddle-node bifurcation. Conditions for the bifurcations allow the identification of correct biasing regimes for the neurons. Also, since the hardware neurons compute in real time, they can be used for dynamic clamp protocols in addition to computational experiments. To empower this analog implementation with the flexibility of a digital simulation, a family of field programmable analog array (FPAA) architectures have been developed in 0.35 um CMOS that provide reconfigurability in the network of neurons as well as tunability of individual neuron parameters. This programmability is obtained using floating-gate (FG) transistors. The neurons are organized in blocks called computational analog blocks (CAB) which are embedded in a programmable switch matrix. An unique feature of the architecture is that the switches, being FG elements, can be used also for computation leading to more than 50,000 analog parameters in 9 sq. mm. Several neural systems including central pattern generators and coincidence detectors are demonstrated. Also, a separate chip that is capable of implementing signal processing algorithms has been designed by modifying the CAB elements to include transconductors, multipliers etc. Several systems including an AM demodulator and a speech processor are presented. An important contribution of this work is developing an architecture for programming the FG elements over a wide dynamic range of currents. An adaptive logarithmic transimpedance amplifier is used for this purpose. This design provides a general solution for wide dynamic range current measurement with a low power dissipation and has been used in imaging chips too. A new generation of integrated circuits have also been designed that are 25 sq. mm in area and contain several new features including adaptive synapses and support for smart sensors. These designs and the previous ones should allow prototyping and rapid development of several neurally inspired systems and pave the path for the design of larger and more complex brain like adaptive neural networks.