Track 1: Cloud, Edge, and Distributed Computing
This track focuses on scalable computing architectures, cloud-native systems, edge intelligence, distributed infrastructures, serverless computing, and resource management.
Topics include: Cloud computing, edge computing, fog computing, distributed computing, serverless systems, microservices, containerisation, virtualisation, cloud security, resource scheduling, and cloud–edge integration.
Track 2: High Performance, Parallel, and Exascale Computing
This track covers advanced computing infrastructures for large-scale scientific and industrial applications.
Topics include: High performance computing, parallel computing, exascale systems, heterogeneous computing, GPU computing, cluster computing, scientific simulations, numerical computing, and performance optimisation.
Track 3: Secure, Trusted, and Privacy-Preserving Computing
This track addresses security, privacy, and trust in modern computing systems.
Topics include: Secure computing, trusted computing, confidential computing, privacy-preserving computing, cryptographic protocols, secure cloud infrastructures, cyber-resilience, data protection, and trustworthy AI infrastructures.
Track 4: Mobile, Ubiquitous, and Human-Centered Computing
This track focuses on computing technologies embedded in daily life, human environments, and mobile systems.
Topics include: Mobile computing, ubiquitous computing, pervasive computing, wearable computing, spatial computing, ambient computing, context-aware systems, human-centered computing, social computing, and intelligent user interfaces.
Track 5: Green and Sustainable Computing
This track focuses on environmentally responsible computing technologies and digital sustainability.
Topics include: Green computing, sustainable computing, low-power computing, carbon-aware computing, energy-efficient data centres, sustainable cloud systems, circular digital technologies, and environmental impact assessment of computing infrastructures.
Track 6: Neuromorphic and Brain-Inspired Computing
This track covers computing models inspired by the structure and function of biological neural systems.
Topics include: Neuromorphic computing, brain-inspired computing, spiking neural networks, neuromorphic chips, event-based computing, synaptic devices, memristive neural systems, and hardware-based artificial intelligence.
Track 7: Quantum Computing and Quantum Information Systems
This track focuses on computational models and technologies based on quantum principles.
Topics include: Quantum computing, quantum algorithms, quantum simulation, quantum machine learning, quantum error correction, quantum circuits, hybrid quantum-classical computing, and quantum software platforms.
Track 8: Photonic, Optical, and Spintronic Computing
This track addresses non-electronic and post-CMOS computing approaches.
Topics include: Photonic computing, optical computing, spintronic computing, light-based processors, optical neural networks, spin-based logic, hybrid photonic-electronic systems, and advanced device architectures.
Track 9: Memristive, In-Memory, and Reconfigurable Computing
This track focuses on architectures that reduce the gap between memory and processing.
Topics include: Memristive computing, in-memory computing, processing-in-memory, reconfigurable computing, FPGA-based computing, non-volatile memory systems, ReRAM-based logic, and hardware acceleration.
Track 10: Bio-Inspired and Unconventional Computing
This track covers non-traditional computing models inspired by biological, chemical, physical, and natural systems.
Track 11: Data Science and Analytics
This track covers data-driven approaches, computational methods, statistical analysis, machine learning, and advanced analytics for extracting knowledge, identifying patterns, and supporting decision-making across scientific and technological fields.
Track 12: Artificial Intelligence and Intelligent Systems
This track focuses on advanced artificial intelligence technologies, intelligent algorithms, autonomous systems, and computational approaches for learning, reasoning, prediction, and decision-making across scientific, industrial, and real-world applications.
Topics include: Artificial intelligence, machine learning, deep learning, generative AI, large language models, foundation models, reinforcement learning, explainable AI, neuro-symbolic AI, multi-agent systems, autonomous intelligent systems, natural language processing, computer vision, evolutionary algorithms, and intelligent decision-making.