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Procrastination Is All You Need: Exponent Indexed Accumulators for Floating Point, Posits and Logarithmic Numbers

arXiv 2406.05866 ↗First posted 9 Jun 2024cs.CV cs.AI cs.ARcited by 0

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Abstract (arXiv)

This paper discusses a simple and effective method for the summation of long sequences of floating point numbers. The method comprises two phases: an accumulation phase where the mantissas of the floating point numbers are added to accumulators indexed by the exponents and a reconstruction phase where the actual summation result is finalised. Various architectural details are given for both FPGAs and ASICs including fusing the operation with a multiplier, creating efficient MACs. Some results are presented for FPGAs, including a tensor core capable of multiplying and accumulating two 4x4 matrices of bfloat16 values every clock cycle using ~6,400 LUTs + 64 DSP48 in AMD FPGAs at 700+ MHz. The method is then extended to posits and logarithmic numbers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 9 Jun 2024.

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References · 2

  1. 2023FP8 versus INT8 for efficient deep learning inferencecited by 31
  2. 2024From a Lossless (~1.5:1) Compression Algorithm for Llama2 7B Weights to Variable Precision, Variable Range, Compressed Numeric Data Types for CNNs and LLMscited by 1

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