Sylvain is an highly experienced R&D engineer with over 30 years of international experience. His skills range from AI model quantization, to optical spectrum analyzer design to mobile communications and telephony design. Sylvain’s expertise lies in both hardware and software DSP, algorithms, and Machine Learning optimization.

Machine Learning Consultant

2021 - Present · SHD Group

Machine Learning, Algorithms, DSP

Principal Engineer

2020 · 0JAS

Machine Learning Engineer

Principal Engineer

2017 - 2019 · Wave Computing

Team lead, Core Technology, AI group

Senior Staff Engineer

2015 - 2016 · Qualcomm

Engineer in the Firmware group, responsible for algorithm designs, simulation, modelisation, and implementation in the area of VDSL.

Principal System Engineer

2003 - 2016 · ikanos

Principal engineer in the system-algorithm team.

Senior Design Engineer

1984 - 2003 · Anritsu (formerly Wiltron)

Hardware engineer. Board design, PLD, FPGA, DSP, telephony.

MSEE, focus on Telecom and DSP

1983 - 1984 · Stanford University

Diplôme d'Ingenieur (5-year degree), Mechanical Engineering, Linear Systems

1978 - 1981 · ENSMM - École Nationale Supérieure de Mécanique et des Microtechniques

DEA (MAS), Optics and Signal Processing

1980 - 1981 · Université de Franche-Comté

Classes Préparatoires

1976 - 1978 · Lycée Pierre-de-Fermat, Toulouse, France

Patents

2019. Flamant, Sylvain. Neural Network Output Layer for Machine Learning. U.S. Patent Application Number 16/459,731, Patent Pending.

2015. Flamant, Sylvain et al. Method and Apparatus for Peak-to-Average Ratio Reduction. U.S. Patent 9,036,730 B2.

Efficient Softmax

Designed a 16-bit fixed point softmax layer for hardware implementation using 3 small lookup tables in order to only use 2 multiplications per output.

Efficient SqueezeNet

Simulated the SqueezeNet NN for low number of bits (8-16 bits) for inference and training.

Simple Batchnorm

Designed a simpler batchnorm block with equivalent performance for hardware implementation.

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