4 citations · 6 across the 6 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
AdaQAT: Adaptive Bit-Width Quantization-Aware Training
Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys +2
Large-scale deep neural networks (DNNs) have achieved remarkable success in many application scenarios. However, high computational complexity and energy costs of modern DNNs make…
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…
Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing
Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys +2
One of the major bottlenecks in high-resolution Earth Observation (EO) space systems is the downlink between the satellite and the ground. Due to hardware limitations, on-board pow…
Approximations in Deep Learning
Etienne Dupuis, Silviu-Ioan Filip, Olivier Sentieys +3
The design and implementation of Deep Learning (DL) models is currently receiving a lot of attention from both industrials and academics. However, the computational workload associ…
Design of Optimal Multiplierless FIR Filters
Martin Kumm, Anastasia Volkova, Silviu-Ioan Filip
This work presents two novel optimization methods based on integer linear programming (ILP) that minimize the number of adders used to implement a direct/transposed finite impulse…
Rational minimax approximation via adaptive barycentric representations
Silviu-Ioan Filip, Yuji Nakatsukasa, Lloyd N. Trefethen +1
Computing rational minimax approximations can be very challenging when there are singularities on or near the interval of approximation - precisely the case where rational function…