7 papers
What is New in Stochastic Rounding: a Survey on Theory, Hardware, and Applications
El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip +1
Stochastic rounding (SR) is a probabilistic method used to round numbers to floating-point and fixed-point representations. In length summation, the worst-case error of SR grow…
Probabilistic Error Analysis of Limited-Precision Stochastic Rounding: Horner's Algorithm and Pairwise Summation
El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip +1
Stochastic rounding (SR) is a probabilistic rounding mode that mitigates errors in large-scale numerical computations, especially when prone to stagnation effects. Beyond numerical…
Mixed precision accumulation for neural network inference guided by componentwise forward error analysis
El-Mehdi El Arar, Silviu-Ioan Filip, Theo Mary +1
This work proposes a mathematically founded mixed precision accumulation strategy for the inference of neural networks. Our strategy is based on a new componentwise forward error a…
SWAPPER: Dynamic Operand Swapping in Non-commutative Approximate Circuits for Online Error Reduction
Marcello Traiola, Nazar Misyats, Silviu-Ioan Filip +2
Error-tolerant applications, such as multimedia processing, machine learning, signal processing, and scientific computing, can produce satisfactory outputs even when approximate co…
Probabilistic error analysis of limited-precision stochastic rounding
El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip +1
Classical probabilistic rounding error analysis is particularly well suited to stochastic rounding (SR), and it yields strong results when dealing with floating-point algorithms th…
A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training
Sami Ben Ali, Silviu-Ioan Filip, Olivier Sentieys
Training Deep Neural Networks (DNNs) can be computationally demanding, particularly when dealing with large models. Recent work has aimed to mitigate this computational challenge b…