Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning
The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, tailored for machine learning. These formats allow efficient representation in few bits, with parameters including width, precision, signedness, and infinities. Operations are exception-free, using stochastic rounding, and signal exceptions via NaN. The standard also introduces kappa-approximation for approximate implementations and is mechanically verified.
[2606.04028] Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning
[Submitted on 1 Jun 2026]
Title:Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning
View a PDF of the paper titled Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning, by Andrew Fitzgibbon and 2 other authors
View PDF
Abstract:The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating machine learning. These formats allow efficient and consistent representation of values in a small number of bits. The defined formats are parameterized over width and precision in bits, signedness, and the presence of infinities. Operations are defined by decoding floating-point values to the set of closed extended reals: the reals augmented with positive and negative infinity and NaN (Not a Number). Explicit treatment of NaN and infinite operands ensures that only real arithmetic is invoked in operation definitions. Extensive rounding and saturation modes are defined; stochastic rounding is included. Operations are exception-free, accelerating throughput, with exceptional situations communicated through return values, e.g., NaN. Operations on blocks of values sharing a common scale factor are defined in terms of the underlying operations in a uniform manner. System vendors may describe approximate implementations via a novel scale-invariant measure, akin to units in the last place, called kappa-approximation. Standard function definitions and various other properties are mechanically verified and generated using formal specifications.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.04028 [cs.LG]
(or arXiv:2606.04028v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.04028
arXiv-issued DOI via DataCite
Submission history
From: Christoph M. Wintersteiger [view email] [v1] Mon, 1 Jun 2026 19:27:39 UTC (24 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning, by Andrew Fitzgibbon and 2 other authors
View PDF
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)