Minisymposium Presentation

The increasing computational demand from big data-intensive domains such as genomics, astronomy, and artificial intelligence accelerates data center (DC) carbon emissions. This trend underscores the need for sustainable computing strategies balancing performance, energy efficiency, and carbon reduction in large computing infrastructures. This talk examines trade-offs in upgrading legacy DCs to meet growing performance demands while aligning with sustainability goals. We analyze the acceleration vs. energy efficiency dilemma of different platform choices and the specialization vs. flexibility balance of CPUs, GPUs, FPGAs, and ASICs. Effective carbon-reduction procurement strategies require considering both operational and capital expenditures. We will introduce a design space exploration framework to support optimal decision-making for DC upgrades and sustainable investments. A key focus will be FPGAs for balancing performance, energy efficiency, and flexibility. We will discuss hardware-software co-design methodologies for reducing time-to-solution in complex workloads. Real-world case studies demonstrate the effectiveness of FPGA-based solutions in genomics and radio astronomy, showcasing tangible energy savings while maintaining high computational throughput. This session provides practical methodologies for designing next-generation, low-carbon computing infrastructures, ensuring DCs can scale without worsening the climate crisis.