The evolution of floating-point computation has been shaped by algorithmic advancements, architectural innovations, and the increasing computational demands of modern technologies, such as artificial intelligence (AI) and high-performance computing (HPC). This paper examines the historical progression of floating-point computation in scientific applications and contextualizes recent trends driven by AI, particularly the adoption of reduced-precision floating-point types. The challenges posed by these trends, including the trade-offs between performance, efficiency, and precision, are discussed, as are innovations in mixed-precision computing and emulation algorithms that offer solutions to these challenges. This paper also explores architectural shifts, including the role of specialized and general-purpose hardware, and how these trends will influence future advancements in scientific computing, energy efficiency, and system design.

翻译：浮点计算的演进由算法进步、架构创新以及现代技术（如人工智能和高性能计算）日益增长的计算需求共同塑造。本文考察了科学应用中浮点计算的历史发展脉络，并阐释了由人工智能驱动的最新趋势，特别是低精度浮点类型的采用。文中讨论了这些趋势带来的挑战，包括性能、效率与精度之间的权衡，同时也探讨了混合精度计算和仿真算法等应对这些挑战的创新技术。本文还探究了架构转变，包括专用硬件与通用硬件的作用，以及这些趋势将如何影响科学计算、能源效率和系统设计的未来发展。