Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
William Andreopoulos
Second Advisor
Katerina Potika
Third Advisor
Robert Chun
Keywords
Machine Learning, Apple Silicon, CUDA, Metal Performance Shaders(MPS), Neuromorphic, Spiking Neural Networks, NVIDIA
Abstract
Machine learning’s computational demands necessitate optimal performance and utilization. This research compares Apple Silicon M3 Pro with MPS, NVIDIA RTX 3070 GPU with CUDA, and neuromorphic computing for machine learning methods. We provide a cross-platform and cross-architecture performance analysis of machine learning methods to identify optimal configurations for training and inference scenarios. On traditional neural networks, Apple Silicon with MPS delivers superior energy efficiency at the cost of longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs. Convolutional spiking neural networks perform competitively on event-based data, particularly on Apple Silicon with MPS, but underperform on frame-based data compared to traditional networks. Results highlight the importance of matching platform-architecture combinations to application requirements.
Recommended Citation
Saini, Ryan, "PERFORMANCE COMPARISON OF MACHINE LEARNING ACROSS METAL, CUDA, AND NEUROMORPHIC FRAMEWORKS" (2025). Master's Projects. 1547.
DOI: https://doi.org/10.31979/etd.zt7b-m38b
https://scholarworks.sjsu.edu/etd_projects/1547
