2024/09/22 by Itamar Cohen, Cohen, Itamar, Gil Einziger +1
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Numerical Methods and Algorithms
paper · pdf · doi:10.48550/arxiv.2410.03692
openalex publication_date 2024/09/22 · openalex created_date 2024/11/01 · openalex updated_date 2026/07/28
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.