Portland State University. Department of Electrical and Computer Engineering
Date of Publication
Master of Science (M.S.) in Electrical and Computer Engineering
Electrical and Computer Engineering
Associative memories, Computing architectures, Memristor-based circuits, Associative storage -- Design and construction, Memristors, Genetic programming (Computer science), Neural networks (Computer science)
1 online resource (xxiv, 132 p.) : col. ill.
Associative Memories (AMs) are essential building blocks for brain-like intelligent computing with applications in artificial vision, speech recognition, artificial intelligence, and robotics. Computations for such applications typically rely on spatial and temporal associations in the input patterns and need to be robust against noise and incomplete patterns. The conventional method for implementing AMs is through Artificial Neural Networks (ANNs). Improving the density of ANN based on conventional circuit elements poses a challenge as devices reach their physical scalability limits. Furthermore, stored information in AMs is vulnerable to destructive input signals. Novel nano-scale components, such as memristors, represent one solution to the density problem. Memristors are non-linear time-dependent circuit elements with an inherently small form factor. However, novel neuromorphic circuits typically use memristors to replace synapses in conventional ANN circuits. This sub-optimal use is primarily because there is no established design methodology to exploit the memristor's non-linear properties in a more encompassing way. The objective of this thesis is to explore denser and more robust AM designs using memristor networks. We hypothesize that such network AMs will be more area-efficient than the traditional ANN designs if we can use the memristor's non-linear property for spatial and time-dependent temporal association. We have built a comprehensive simulation framework that employs Genetic Programming (GP) to evolve AM circuits with memristors. The framework is based on the ParadisEO metaheuristics API and uses ngspice for the circuit evaluation. Our results show that we can evolve efficient memristor-based networks that have the potential to replace conventional ANNs used for AMs. We obtained AMs that a) can learn spatial and temporal correlation in the input patterns; b) optimize the trade-off between the size and the accuracy of the circuits; and c) are robust against destructive noise in the inputs. This robustness was achieved at the expense of additional components in the network. We have shown that automated circuit discovery is a promising tool for memristor-based circuits. Future work will focus on evolving circuits that can be used as a building block for more complicated intelligent computing architectures.
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Sinha, Arpita, "Evolving Nano-scale Associative Memories with Memristors" (2011). Dissertations and Theses. Paper 445.