The HiPEAC conference is the premier European forum for experts in computer architecture, programming models, compilers and operating systems for general-purpose, embedded and cyber-physical systems. Areas of focus and integration include safety-critical dependencies, cybersecurity, energy efficiency and machine learning.
Spanning the compute continuum from edge to cloud, HiPEAC is a network of around 2,000 world-class computing systems researchers, industry representatives and students.
- Monday, Jan 20: Excellence in Artificial Intelligence and Edge Computing
The convergence of AI and edge computing represents a new era of technological advances, where the immediacy of data processing and the sophistication of AI algorithms lead to new products and services. Industries are rapidly adopting edge AI applications to streamline costs, automate complex processes, enhance decision-making capabilities, and refine operational efficiency. These applications are not just theoretical constructs; they are real-world solutions that are reshaping industries.
This workshop organized by us is dedicated to exploring the frontiers of the AI and edge computing synergy, focusing on the development of scalable and robust AI systems. In order to make it more interactive and lively, there will be a matchmaking session and a demo session. In the latter, participants will be able to show live demos, either on-site or with a video, of their research and results.
- Tuesday, Jan 21: AccML: Accelerated Machine Learning
This 7th AccML workshop aims to bring together researchers working in Machine Learning and System Architecture to discuss requirements, opportunities, challenges and next steps in developing novel approaches for machine learning systems.
The remarkable performance achieved in a variety of application areas (natural language processing, computer vision, games, etc.) has led to the emergence of heterogeneous architectures to accelerate machine learning workloads. In parallel, production deployment, model complexity and diversity pushed for higher productivity systems, more powerful programming abstractions, software and system architectures, dedicated runtime systems and numerical libraries, deployment and analysis tools.
The high level of interest in these areas calls for a dedicated forum to discuss emerging acceleration techniques and computation paradigms for machine learning algorithms, as well as the applications of machine learning to the construction of such systems.