activity
20192026
most citedApproximations in Deep Learning

4 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

math.NA2026

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…

cs.AR2025

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…

cs.LG2025

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…

cs.AR20224 cited

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…

eess.SP20192 cited

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…