Author

Ryan Saini

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.

Available for download on Monday, May 25, 2026

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