RESEARCH

NLP

Efficient, arbitrarily high precision hardware logarithmic arithmetic for linear algebra

May 15, 2020

Abstract

The logarithmic number system (LNS) is arguably not broadly used due to exponential circuit overheads for summation tables relative to arithmetic precision. Methods to reduce this overhead have been proposed, yet still yield designs with high chip area and power requirements. Use remains limited to lower precision or high multiply/add ratio cases, while much of linear algebra (near 1:1 multiply/add ratio) does not qualify. We present a dual-base approximate logarithmic arithmetic comparable to floating point in use, yet unlike LNS it is easily fully pipelined, extendable to arbitrary precision with O(n^2) overhead, and energy efficient at a 1:1 multiply/add ratio. Compared to float32 or float64 vector inner product with FMA, our design is respectively 2.3× and 4.6× more energy efficient in 7 nm CMOS. It depends on exp and log evaluation 5.4× and 3.2× more energy efficient, at 0.23× and 0.37× the chip area for equivalent accuracy versus standard hyperbolic CORDIC using shift-and-add and approximated ODE integration in the style of Revol and Yakoubsohn. This technique is a novel alternative for low power, high precision hardened linear algebra in computer vision, graphics and machine learning applications.

Download the Paper

AUTHORS

Written by

Jeff Johnson

Publisher

IEEE Symposium on Computer Arithmetic

Related Publications

December 26, 2025

REINFORCEMENT LEARNING

NLP

Safety Alignment of LMs via Non-cooperative Games

Anselm Paulus, Ilia Kulikov, Brandon Amos, Remi Munos, Ivan Evtimov, Kamalika Chaudhuri, Arman Zharmagambetov

December 26, 2025

December 18, 2025

NLP

How Good is Post-Hoc Watermarking With Language Model Rephrasing?

Pierre Fernandez, Tom Sander, Hady Elsahar, Hongyan Chang, Tomáš Souček, Sylvestre Rebuffi, Valeriu Lacatusu, Tuan Tran, Alexandre Mourachko

December 18, 2025

December 18, 2025

RESEARCH

COMPUTER VISION

Pixel Seal: Adversarial-only training for invisible image and video watermarking

Tomáš Souček, Pierre Fernandez, Hady Elsahar, Sylvestre Rebuffi, Valeriu Lacatusu, Tuan Tran, Tom Sander, Alexandre Mourachko

December 18, 2025

December 12, 2025

NLP

COMPUTER VISION

Text-Guided Semantic Image Encoder

Raghuveer Thirukovalluru, Xiaochuang Han, Bhuwan Dhingra, Emily Dinan, Maha Elbayad

December 12, 2025

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.