Theme #1:
Architecture and design of reconfigurable computing devices, Novel fabrics beyond traditional FPGAs, eFPGAs, Chiplet-based FPGAs, Improving the programmability of reconfigurable computing devices
Theme #2:
Hardware acceleration of machine learning (ML), Domain-specific acceleration of emerging workloads, New applications of reconfigurable computing such as digital twins and quantum control and simulation
Theme #3:
Machine Learning (ML) for Hardware, Deploying ML and LLMs to improve architecture, design and verification of ASICs or FPGAs, developing datasets and frameworks for using ML easily in chip design
Theme #4:
Sustainable Computing, Energy efficient computing, Processing in/near/using memory, Combining non-Von Neumann computing with existing paradigms
