The 15th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART2025)

The 15th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART2025) focuses on research and advancements in computing with acceleration technologies, emphasizing efficiency in high-performance computing, data centers, mobile, and IoT systems.

Key Topics

• Quantum-classical hybrid computing
• Efficient models and algorithms for LLM
• Accelerator and system-level architecture for LLM
• Benchmarks for ML/LLM
• Domain-specific architectures
• Neuromorphic computing and architecture
• Programming paradigms, languages, and frameworks
• Runtime methodologies for heterogeneous systems
• Compilers and high-level synthesis tools
• Quantum programming and software
• Algorithms for quantum computing
• Applications with efficient LLM/Neuromorphic accelerators
• Performance evaluation and analysis
• Comparisons between accelerator technologies
• Novel systems for quantum computing: qubit simulation, operation, and error correction
• High-performance computing with quantum technologies
• Edge technologies for LLM
• AI-supported code generation/debugging
• Application examples that benefit from efficient acceleration
• Heterogeneous processor architectures and systems for high-performance and low-power
• Fault-tolerant quantum computing
• Complete systems showing increased energy efficiency and performance
• AI for science with higher productivity
• LLM-based design method
• Novel systems and platforms based on FPGA, GPU, and other devices

Who should Attend

• Quantum computer architecture researchers
• Scientists working on high-performance computing with quantum technologies
• Experts in quantum programming and software
• Professionals interested in quantum-classical hybrid computing
• Researchers in fault-tolerant quantum computing
• Developers of runtime methodologies for heterogeneous systems
• AI specialists working on efficient models and algorithms
• System architects designing accelerators for LLM
• Engineers in neuromorphic computing and architecture
• Experts in compilers and high-level synthesis tools
• Industry professionals focused on domain-specific architectures
• Academics studying benchmarks for ML/LLM
• Software developers exploring AI-supported code generation/debugging
• Scientists designing application-specific accelerator technologies
• Technology leaders in heterogeneous processor architectures