Analog Computing Arrays (ACAs) provide a computation system capable of performing a large number of multiply and add operations in an analog form. This system can therefore implement several computation algorithms that are currently realized using Digital Signal Processors (DSPs) who have an analogues accumulate and add functionality. DSPs are generally preferred for signal processing because they provide an environment that permits programmability once fabricated. ACA systems propose to offer similar functionality by providing a programmable and reconfigurable analog system. ACAs inherent parallelism and analog efficiency present several advantages over DSP implementations of the same systems. The computation power of an ACA system is directly proportional to the number of computing elements used in the system. Array size is limited by the number of computation elements that can be managed in an array. This number is continually growing and as a result, is permitting the realization of signal processing systems such as real-time speech recognition, image processing, and many other matrix like computation systems. This research provides a systematic process to implement, program, and use the computation elements in large-scale Analog Computing Arrays. This infrastructure facilitates the incorporation of ACA without the current headaches of programming large arrays of analog floating-gates from off-chip, currently using multiple power supplies, expensive FPGA controllers/computers, and custom Printed Circuit Board (PCB) systems. Proof of the flexibility and usefulness of ACAs has been demonstrated by the construction of two systems, an Analog Fourier Transform and a Vector Quantizer.

This research paper focuses on analog computers and creating a modular low-cost analog computer system in a single board computer form factor. The single-board analog computer will have the capacity to solve second-order differential equations. The capabilities and possibilities of the single board Analog computer will be explored as well as analog computing in general. The paper follows design science research methodology (DSRM) with the goal of creating and evaluating a working artifact. The artifacts' functionality is evaluated based on a demonstration of its ability to solve Mathieu’s differential equation as well as simulate a spring-mass dampening system. This paper proves that it is possible to create a low-cost analog computer in a modern form factor. The artifact is also placed in a larger contextual setting based on the empirical material provided where its value of it in a digital society is presented. For the world to continue its progression in computational power, but still, limit the already high energy usage, a drastic change is needed. This paper suggests adapting to analog/hybrid technology. To further the progression of analog/hybrid technology it must be made accessible to a wider group of people compared to today. The artifact in this paper offers a solution to this.

This book documents an exploration of emergent and linear modes of defining space, form, and structure. The thesis highlights a dialog between analog and digital modeling techniques, in concept and project development. It identifies that analog modeling techniques, coupled with judgment, can be used to develop complex forms. The thesis project employs critical judgment and the textile techniques of crochet as a vehicle generate form. Crochet lends itself to this investigation because it is a serial process of fabrication that allows for the introduction of specific non-linear modifications. The resulting emergent forms produced by this mode of working can be precisely described by digital modeling techniques. These analog crochet models are translated into the digital through the employment of advanced digital modeling tools. This translation enables the visualization, development, testing, and execution of an architectural space, form, and structure. / Master of Architecture

The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.

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