Energy-Efficient Low-Power Edge AI for Time-Series Data with Optimized RNNs- Group 2

Introduction
Time-series data, characterized by sequential observations over time, is fundamental across diverse domains, including healthcare, environmental monitoring, predictive maintenance, and behavior analysis. Efficient processing of time-series data poses unique challenges due to its temporal dependencies, variability, and high-dimensional nature. Recent advancements in lightweight neural networks and edge AI offer opportunities to tackle these challenges by enabling real-time, resource-efficient analysis directly at the edge, eliminating the need for costly cloud infrastructure. This project focuses on developing optimized models and systems for processing time-series data, emphasizing scalability, energy efficiency, and real-world applicability.
Motivation
Many industries rely on time-series data to derive actionable insights and make data-driven decisions. However, traditional approaches often demand high computational power, large memory resources, and consistent connectivity, limiting their deployment in resource-constrained or remote environments. By leveraging optimized machine learning models and edge computing, it is possible to design systems that provide real-time analysis while maintaining low energy consumption and operational cost. Such systems can unlock scalable solutions across various sectors, from industrial automation to personalized healthcare.
Objectives

1. Model Development: Design machine learning models, such as lightweight recurrent neural networks (RNNs) or transformer-based architectures, optimized for analyzing time-series data with minimal memory and computational requirements.
2. Hardware Deployment: Implement these models on low-power edge devices, such as microcontrollers, FPGAs, or dedicated AI accelerators, utilizing quantization and pruning techniques for efficiency.
3. Application Testing: Evaluate the system's performance in multiple use cases, including but not limited to predictive maintenance, environmental monitoring, and activity recognition, focusing on accuracy, energy efficiency, and scalability

Expected Outcomes
This project aims to establish a robust framework for time-series data analysis, with the following key deliverables:

• Optimized algorithms for real-time processing of time-series data with high accuracy and low resource usage.
• Energy-efficient edge AI systems capable of functioning in remote or resource-constrained environments.
• Validation across diverse applications, demonstrating scalability and adaptability to different domains.

Conclusion
By focusing on resource-efficient processing of time-series data, this project bridges the gap between cutting-edge machine learning and practical deployment. It delivers scalable, low-power solutions applicable to a wide range of industries, enabling real-time insights without reliance on expensive cloud infrastructure or high-power devices.