Internship - R&D - Event Signal Processing versus Spiking Accelerators
il y a 4 semaines
Internship Opportunities at Prophesee
Prophesee is a dynamic company that develops computer vision sensors and systems for various applications. We are looking for passionate interns who can contribute to our research and development team.
Event-Based Technology
Our event-based cameras are bio-inspired and free from the concept of images. They capture information asynchronously, resulting in a sparse output that enables real-time processing. We are advancing the algorithmic and machine learning side of this new kind of machine vision, enabling our clients to build new applications in various domains.
Internship Position Description
We are seeking interns who demonstrate initiative, take ownership of project work, and exhibit a strong spirit of innovation. The ideal candidate is a curious and creative individual with prior experience in C++/Python programming and exposure to computer vision/image processing/artificial intelligence/machine learning domains.
Main Required Skills
- Excellent programming skills in C++ and/or Python
- Engineering background in Computer Science, Mathematics, or related field
- Prior experience in projects involving algorithmic design, 3D vision, machine learning, numerical optimization, etc.
R&D
Event Signal Processing versus Spiking Accelerators
Event sensors generate sparse and asynchronous data that are not compatible with traditional computers. To scale with larger pixel arrays, the bandwidth of these sensors has increased, and low-level filtering close to the pixels is necessary. Many hardware accelerators have been proposed to ease event processing, using FPGAs or dedicated ASICs.
Convolutional neural networks are mostly sparse after a few layers, and some hardware accelerators already use this feature to speed up computations. However, the input information size and frequency must be defined and fixed. Spiking Neural Networks (SNN), which can be seen as asynchronous recurrent neural networks, add asynchronous time-based processing to traditional neural networks, making them suitable for event-based data. The ability to program SNN accelerators will enable application-specific filtering.
Purpose of the Internship
The purpose of this internship is to implement low-level event processing using SNN accelerators. The plan is to analyze the SNN accelerator/processing state of the art, implement software for low-level processing functions to filter the event stream using SNN, and implement hardware using a SNN accelerator from Prophesee partners.
Benefits
Salary package: 80% SMIC (9.32 €/h)
Meal vouchers: 9 €/working day (Swile card)
Working hours: 35 h/week
Office location: Paris Bastille (74 rue du Faubourg Saint Antoine 75012)
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