Special Session 1
Advances in Machine Learning for Constrained Resource Systems
Organizers: Prof. Anastacia B. Alvarez, PhD, University of the Philippines, Philippines
Prof. Rhandley D. Cajote, PhD, University of the Philippines, Philippines
Introduction: This special session focuses on advancements in the application implementation of machine learning (ML)
techniques to systems with constrained resources, such as edge devices, IoT networks, mobile platforms, and
embedded systems. These systems often face limitations in terms of computational power, memory, energy, and
bandwidth, making the deployment of traditional machine learning algorithms challenging. The session will explore
ML methodologies, optimization strategies, and architectural innovations that enable efficient processing,
real-time decision-making, and enhanced performance in such resource-constrained environments. Topics include
lightweight neural networks, model compression, federated learning, energy-efficient algorithms, and edge AI
applications.
As the Internet of Things (IoT) and edge computing continue to proliferate, systems with limited
computational resources are becoming increasingly prevalent in diverse fields such as healthcare, automotive, and
industrial automation. Traditional ML models, which often demand high computational power and memory, are
not feasible in these contexts. This session addresses the need for innovative machine learning solutions that can
operate effectively under these constraints. By bringing together experts in ML, hardware, and embedded systems,
the session aims to accelerate the development of next-generation intelligent systems that are both
resource-efficient and scalable.
Keywords： Machine Learning, IoT, WSN, Limited Resource
Submission：https://easychair.org/conferences/?conf=amlds2025
About the Organizers:
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Dr. Alvarez is currently a full Professor at the Electrical and Electronics Engineering Institute where she is affiliated with the Microelectronics and Microprocessors Laboratory. She finished her PhD in Electrical and Computer Engineering at the National University of Singapore in 2017. Her research interest is in the field of digital integrated circuits, focusing mainly on energy efficient techniques for energy-limited Internet of Things application. |
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Dr. Cajote is a full professor at the Electrical and Electronics Engineering Institute at the University of the Philippines in Diliman. He is the head of the Digital Signal Processing Laboratory of the Institute. His research interest includes signal and image processing, video coding and communications, machine learning systems and artificial intelligence. |