提出可调精度范围的浮点数表示，兼顾大范围计数与高精度。
Floating-floating point: a highly accurate number representation with flexible Counting ranges
- 通过动态调整尾数与指数位分配，实现灵活精度。
- 在选定子范围内精度优于现有8位浮点方案。
- 适合联邦学习与网络测量等资源受限场景。
高效数值表示对联邦学习、自然语言处理和网络测量至关重要。受限于时序、面积和功耗，这些应用常采用窄位宽（如8位）数值系统。现有浮点系统在计数范围与精度间存在权衡。本文提出浮点浮点数（F2P），通过动态调整尾数与指数的划分比例，实现大计数范围与特定子范围内的更高精度。评估表明，相较于当前最优方案，采用F2P可提升网络测量准确率与联邦学习性能。
原文摘要 · Abstract (English)
Efficient number representation is essential for federated learning, natural language processing, and network measurement solutions. Due to timing, area, and power constraints, such applications use narrow bit-width (e.g., 8-bit) number systems. The widely used floating-point systems exhibit a trade-off between the counting range and accuracy. This paper introduces Floating-Floating-Point (F2P) - a floating point number that varies the partition between mantissa and exponent. Such flexibility leads to a large counting range combined with improved accuracy over a selected sub-range. Our evaluation demonstrates that moving to F2P from the state-of-the-art improves network measurement accuracy and federated learning.
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