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2020 – today
- 2024
[c9]Léo Pradels, Silviu-Ioan Filip, Olivier Sentieys, Daniel Chillet, Thibaut Le Calloch:
FPGA-based CNN Acceleration using Pattern-Aware Pruning. AICAS 2024: 228-232- [c8]Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys, Clément Coggiola, Mickael Bruno:
AdaQAT: Adaptive Bit-Width Quantization-Aware Training. AICAS 2024: 442-446 - [c7]Sami Ben Ali, Silviu-Ioan Filip, Olivier Sentieys:
A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training. DATE 2024: 1-6 - [i5]Sami Ben Ali, Silviu-Ioan Filip, Olivier Sentieys:
A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training. CoRR abs/2404.14010 (2024) - [i4]Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys, Clément Coggiola, Mickael Bruno:
AdaQAT: Adaptive Bit-Width Quantization-Aware Training. CoRR abs/2404.16876 (2024) - [i3]El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip, Mantas Mikaitis:
Probabilistic error analysis of limited-precision stochastic rounding. CoRR abs/2408.03069 (2024) - 2023
- [j5]Martin Kumm
, Anastasia Volkova, Silviu-Ioan Filip:
Design of Optimal Multiplierless FIR Filters With Minimal Number of Adders. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(2): 658-671 (2023) - [c6]Nicolas Brisebarre, Silviu-Ioan Filip:
Towards Machine-Efficient Rational L∞-Approximations of Mathematical Functions. ARITH 2023: 119-126 - [i2]Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys, Clément Coggiola, Mickael Bruno:
Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing. CoRR abs/2311.11172 (2023) - 2022
- [c5]Mariko Tatsumi, Silviu-Ioan Filip, Caroline White, Olivier Sentieys, Guy Lemieux:
Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training. FPT 2022: 1-9 - [i1]Etienne Dupuis, Silviu-Ioan Filip, Olivier Sentieys, David Novo, Ian O'Connor, Alberto Bosio:
Approximations in Deep Learning. CoRR abs/2212.04297 (2022) - 2021
- [c4]Florent de Dinechin, Silviu-Ioan Filip, Martin Kumm, Anastasia Volkova:
Towards Arithmetic-Centered Filter Design. ARITH 2021: 115-118 - [c3]Olivier Sentieys, Silviu-Ioan Filip, David Briand, David Novo, Etienne Dupuis, Ian O'Connor, Alberto Bosio:
AdequateDL: Approximating Deep Learning Accelerators. DDECS 2021: 37-40
2010 – 2019
- 2019
- [j4]Silviu-Ioan Filip, Aurya Javeed, Lloyd N. Trefethen:
Smooth Random Functions, Random ODEs, and Gaussian Processes. SIAM Rev. 61(1): 185-205 (2019) - [c2]Florent de Dinechin, Silviu-Ioan Filip, Martin Kumm, Luc Forget:
Table-Based versus Shift-And-Add Constant Multipliers for FPGAs. ARITH 2019: 151-158 - 2018
- [j3]Silviu-Ioan Filip, Yuji Nakatsukasa, Lloyd N. Trefethen, Bernhard Beckermann:
Rational Minimax Approximation via Adaptive Barycentric Representations. SIAM J. Sci. Comput. 40(4): A2427-A2455 (2018) - [j2]Nicolas Brisebarre, Silviu-Ioan Filip, Guillaume Hanrot:
A Lattice Basis Reduction Approach for the Design of Finite Wordlength FIR Filters. IEEE Trans. Signal Process. 66(10): 2673-2684 (2018) - [c1]Nicolas Brisebarre, George A. Constantinides, Milos Ercezovac, Silviu-Ioan Filip, Matei Istoan, Jean-Michel Muller:
A High Throughput Polynomial and Rational Function Approximations Evaluator. ARITH 2018: 99-106 - 2016
- [b1]Silviu-Ioan Filip:
Robust tools for weighted Chebyshev approximation and applications to digital filter design. (Outils robustes pour l'approximation de Chebyshev pondérée et applications à la synthèse de filtres numériques). University of Lyon, France, 2016 - [j1]Silviu-Ioan Filip:
A Robust and Scalable Implementation of the Parks-McClellan Algorithm for Designing FIR Filters. ACM Trans. Math. Softw. 43(1): 7:1-7:24 (2016)
Coauthor Index
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last updated on 2024-10-01 21:41 CEST by the dblp team
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