Summary of Procrastination Is All You Need: Exponent Indexed Accumulators For Floating Point, Posits and Logarithmic Numbers, by Vincenzo Liguori
Procrastination Is All You Need: Exponent Indexed Accumulators for Floating Point, Posits and Logarithmic Numbers
by Vincenzo Liguori
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for efficiently summing long sequences of floating-point numbers, comprising two phases: accumulation and reconstruction. The method leverages architectural details on both FPGAs and ASICs to optimize the process. Specifically, fusing the operation with a multiplier and creating efficient MACs enable improved performance. The authors present results for FPGAs, showcasing a tensor core that can perform matrix multiplications and accumulations at 700+ MHz using ~6,400 LUTs + 64 DSP48 in AMD FPGAs. The approach is then extended to posits and logarithmic numbers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explains how to quickly add up lots of floating-point numbers. It works by breaking the process into two parts: adding up the parts that don’t change much, and then combining them all together. This makes it faster and more efficient. The authors talk about how they made this work on special computers called FPGAs and ASICs. They even show an example of a really fast part of their design that can do lots of calculations at once. |
