| Autor: |
Jones, Alexander |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Druh dokumentu: |
Text |
| Popis: |
The work shown in this dissertation demonstrates the implementation of a specialized neural network for associative memory within novel neuromorphic hardware. The architecture is implemented using CMOS-based circuitry for information processing and memristive devices for the network’s memory. The architecture is based on a non-Von Neumann version of computer architecture called in-memory computing where information storage and processing reside within a single location. The CMOS circuitry within the architecture has both digital and analog components to perform processing. The memristive devices used in the architecture are a newer form of memristive device that possesses a gate that is used to potentiate/depress the device. These gated-memristive devices allow for simpler hardware architectures for tasks such as reading/writing to a device simultaneously. The architecture demonstrated here uses a property that is often seen within various memristive devices where the state is semi-volatile. This semi-volatile state can be used in tandem with a spiking neuromorphic architecture to perform unique tasks during learning depending on the degree of volatility in the device. Once memories are programmed into the network, it can then later recall previously seen memories by observing partial information from them and performing pattern completion. The final portion of this dissertation focuses on studying how the network behaves when exposed to a larger dataset of information over time and analyzing how the network performs recall on that data. An array of metrics will be used to evaluate the network’s performance during these tests, and potential expansions of network functionality are explored and studied in order to enhance its capabilities in certain applications. |
| Databáze: |
Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|
