UC Irvine

The increasing demand for real-time, low-power, and intelligent systems has led to the development of edge computing, which involves processing data at the source directly from sensors rather than in the cloud or a centralized data center. Neuromorphic hardware, inspired by the structure and function of the biological nervous system, offers unique advantages for edge computing such as parallel processing, event-driven computation, and low power consumption with in memory computing. In memory computing enables the ability to perform online learning where learning can happen simultaneously with data input in real-time rather than the typical offline learning approach of presenting data in large batches for processing and then learning with gradient descent. With online learning, rapid adaptation to the unfamiliar becomes possible which is necessary for many applications such as user customization, and mobile robot navigation in unknown environments. Here we will demonstrate the machine learning algorithms we developed for use with neuromorphic hardware for online learning and rapid adaptation to unfamiliar data streamed from event-based sensors.

The thesis begins by providing an overview of the state-of-the-art in edge computing and neuromorphic hardware, highlighting their strengths and limitations for machine learning. Next, we present our original contributions, including the development of new algorithms for learning on edge neuromorphic hardware, the implementation of these algorithms on state-of-the-art hardware platforms, and the evaluation of their performance in comparison to traditional machine learning algorithms.Our results demonstrate that edge neuromorphic hardware is a promising platform for machine learning, offering high performance, low latency, and low power consumption. Our algorithms are shown to be effective in real-world applications such as image classification, and mid-air gesture recognition. In conclusion, this thesis makes a significant contribution to the field of edge computing and neuromorphic hardware by demonstrating the feasibility of using neuromorphic hardware for learning at the edge. Our work lays the foundation for the development of efficient, low-power, and real-time intelligent systems for edge applications.