Implementation of Machine Learning on Low Power Microcontrollers
Description
Machine learning has been increasingly integrated into several new areas, namely those related to vision processing and language learning models. These implementations of these processes in new products have demanded increasingly more expensive memory usage and computational requirements. Microcontrollers can lower this increasing cost. However, implementation of such a system on a microcontroller is difficult and has to be culled appropriately in order to find the right balance between optimization of the system and allocation of resources present in the system. A proof of concept that these algorithms can be implemented on such as system will be attempted in order to find points of contention of the construction of such a system on such limited hardware, as well as the steps taken to enable the usage of machine learning onto a limited system such as the general purpose MSP430 from Texas Instruments.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024-05
Agent
- Author (aut): Malcolm, Ian
- Thesis director: Allee, David
- Committee member: Spanias, Andreas
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Electrical Engineering Program