Session Overview

Rapid advancements in IoT devices have increased demand for intelligent and autonomous decision making at the edge network. Traditional cloud-based AI systems have become a bottleneck due to modern latency and privacy requirements, especially in IoT deployments. Autonomous AI agents can address these challenges through reasoning, planning, and context-aware capabilities.

However, deployment of AI agents on resource-constrained hardware remains an open and largely unexplored challenge. Key barriers include computational cost, inference latency, multi-agent coordination, fairness, and privacy, especially at large and complex scale.

This special session addresses these challenges through artificial intelligence, autonomous agentic architecture, edge-deployed IoT, and blockchain-enabled decentralized agent coordination. It brings together researchers working on neural network compression, multi-agent coordination, fairness-aware deployment, and cross-domain agentic applications to define the roadmap for future AI systems design and deployment.

Topics of Interest

• Agentic Architectures for Edge and IoT
• Neural Network Compression for Agentic Models
• Multi-Agent Coordination and Management
• Federated and Decentralized Agent Learning
• Fairness, Safety, and Accountability of Deployed Agents
• Explainability and Interpretability of Agent Decisions in IoT Applications
• Blockchain-Based Audit Trails of Agentic Systems
• IoT-Based Applications of Autonomous Agents
• Security, Privacy, and Trust of AI Agents
• Governance of Large and Complex AI Agents in a Smart City Environment

Session Overview

The rapid growth of Artificial Intelligence (AI), intelligent computing systems, and emerging hardware technologies is transforming the future of modern computing infrastructures. From edge devices and IoT platforms to data centers and smart autonomous systems, the demand for intelligent, adaptive, energy-efficient, and trustworthy computing solutions continues to grow significantly. At the same time, emerging challenges related to scalability, reliability, explainability, sustainability, and hardware limitations require innovative interdisciplinary solutions that integrate AI algorithms with advanced computing architectures and intelligent system design.

This special session focuses on recent advances in AI-driven intelligent computing and emerging systems, bringing together researchers from academia and industry working on intelligent architectures, adaptive hardware-aware AI systems, next-generation memory and computing technologies, edge intelligence, and sustainable AI solutions. The session aims to provide a platform for discussing innovative methodologies, practical applications, and future research directions that bridge the gap between AI algorithms, intelligent hardware systems, and real-world deployment environments.

Particular emphasis will be placed on emerging topics such as reinforcement learning for adaptive systems, intelligent memory architectures, generative AI for smart applications, trustworthy and explainable AI, edge intelligence, and energy-efficient computing frameworks. The session welcomes both theoretical and application-oriented contributions addressing the future of intelligent computing systems across healthcare, smart cities, autonomous systems, cybersecurity, IoT, and next-generation computing environments.

Topics of Interest

• AI-Driven Computing Architectures
• Intelligent Memory and Emerging Hardware Systems
• Hardware-Aware Artificial Intelligence
• Reinforcement Learning for Adaptive and Autonomous Systems
• Edge AI and IoT Intelligence
• Energy-Efficient and Sustainable AI Systems
• Generative AI for Smart Applications
• Error-Resilient AI Systems
• Explainable and Trustworthy AI
• AI for Smart Cities and Intelligent Infrastructure
• AI-Driven Optimization in Computing Systems
• Intelligent Data Processing and Decision-Making Systems
• Machine Learning for Emerging Computing Platforms
• AI Applications in Cyber-Physical Systems
• Scalable and Reliable Intelligent Systems
• AI for Healthcare and Biomedical Systems
• Security, Privacy, and Reliability in Intelligent Systems
• AI-Enabled Embedded and Real-Time Systems
• Adaptive and Self-Optimizing Computing Systems