ACCEPTED TUTORIALS
22nd International Conference on Advanced Visual and Signal-Based Systems
Designing and Assembling Smart Cyber-Physical Systems
ILENIA FICILI - Department of Engineering, University of Messina, Italy
MAURIZIO GIACOBBE - Department of Mathematics and Computer Sciences, Physical
Sciences and Earth Sciences, University of Messina, Italy
GIUSEPPE TRICOMI - Department of Engineering, University of Messina, Italy
ANTONIO PULIAFITO - Department of Engineering, University of Messina, Italy
MAIN ARGUMENTS and KEY TOPICS
1) Core Architectures and Concepts
Cyber-Physical Systems (CPSs):
● Systems integrating physical environments
(sensors/actuators) with cyber components (computation/networking).
● Computing Continuum: A seamless environment where tasks are executed across Cloud,
Fog, and Edge devices to optimize performance.
● Systems of Systems: A methodology treating CPS as a complex integration of multiple
independent systems that must be managed.
● IoT as Full-fledged Resources: Integration of Internet of Things devices as standard
computing, storage, and networking assets.
2) Deployment and Operational Paradigms
●Deviceless Paradigm: Exploiting device resources on demand without static preparation or
manual configuration.
● Virtualization and Composition: Abstracting sensors, boards, and edge devices to create a flexible infrastructure for services and applications.
● Edge-to-Cloud AI Integration: Deploying Artificial Intelligence models throughout the
continuum to enable autonomous decision-making and data analytics at the source.
● Functional Pipelines: Executing application elements as dynamic sequences (pipelines)
directly at the Edge.
3) Optimization and Management:
● Latency Minimization: Reducing delays by executing tasks near the physical elements (at the Edge).
● Bandwidth Consumption Optimization: Processing data locally to avoid unnecessary data
transfer to the Cloud.
● Hierarchical Coordination: Edge: Local task execution.
● Fog Nodes: Local coordination and resource management.
● Cloud: Compute-intensive activities and overall system orchestration.
● Anomaly Anticipation: Using AI to predict issues and adjust to environmental changes
instantly.
● Educational and Methodological Tools
● Heterogeneous and Green Infrastructures: Focus on diverse and sustainable technological setups for smart cities and industries.
● Hands-on Experiments: Practical application of theory through concrete examples.
Software Repository:
● Availability of ready-to-use modules, components, and applications
for remote and continuous learning.
Maximize the throughput of your open-source model inference workloads
Andrea Pilzer: Solution Architect at NVIDIA leading the NVIDIA AI Technology Center in Italy where he focuses on supporting researchers on HPC clusters and NVIDIA technology. His main interests are in deep learning, video processing, VLMs and uncertainty estimation. He was postdoc at Aalto University working on uncertainty estimation for deep learning, worked at Huawei Ireland and got his Ph.D. in CS from University of Trento working with Nicu Sebe and Elisa Ricci.
Inference is at its inflection point, agentic AI is pushing inference compute demands beyond traditional limits.
For multimodal models, inference is even more demanding than for text only models (i.e. LLMs). In this tutorial we will leverage the NVIDIA Model-Optimizer library to apply simple and effective optimization techniques to boost your local model throughput. More in detail we will focus on quantization to reduce computational precision without significant accuracy loss, KV-cache compression to alleviate memory bandwidth bottlenecks, and speculative decoding to parallelize token generation and cut latency.
