Neural and analog computation on reconfigurable mixed-signal platforms
Nease, Stephen H.
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This work addresses neural and analog computation on reconfigurable mixed-signal platforms. Many engineered systems could gain tremendous benefits by emulating neural systems. For example, neural systems are incredibly power efficient and fault-tolerant. They are also capable of types of computation that we cannot yet match with conventional computers. Neuromorphic engineers typically implement neural computation using analog circuits because they are low-power and naturally model some aspects of neurobiology. One problem with analog circuits is that they are typically inflexible. To address this shortcoming, our lab has developed reconfigurable analog systems known as Field Programmable Analog Arrays (FPAAs). This dissertation consists of two main parts. The first is the implementation of neural and analog circuits on FPAAs. We first implemented an adaptive winner-take-all circuit, which could model attention in neural systems. Next, we modeled the dendrite, which is the conductive tissue that relays inputs from synapses to the neuron cell body. We also implemented a subtractive music synthesizer, perhaps providing the electronic music synthesis community with a good platform for experimentation. Finally, we conducted a number of neural learning experiments on a neuromorphic platform. The second part of this dissertation includes design aspects of new FPAAs, including configurable blocks that can be used as current-mode DACs in a digitally-enhanced FPAA, the RASP 2.9v. We also consider the design of a new neuromorphic platform containing 256 neurons and over 200,000 synapses, many with learning capability. We also created an active delay line that could be used for beamforming or FIR filter applications. In summary, this work adds to the field of reconfigurable systems by both showing how to implement circuits with them and creating new systems based on lessons learned while working with previous systems.