Internship - R&D - Event Signal Processing versus Spiking Accelerators

Il y a 6 mois

Paris, France Prophesee Temps plein

Internship opportunities at Prophesee

PROPHESEE

Founded by the world’s leading pioneers in the field of neuromorphic vision, Prophesee develops computer vision sensors and systems for application in all fields of artificial vision. The sensor technology is inspired by biological eyes, acquiring visual information in an extremely performing yet highly efficient way. Prophesee’s disruptive vision sensor technology entirely overthrows the established paradigms of frame-based vision acquisition currently used everywhere in computer vision.

This is a great opportunity to join a dynamic company and an exciting team and to lead a paradigm shift in computer vision across many industries.

EVENT-BASED TECHNOLOGY

Prophesee designs and produces a new type of cameras that are bio-inspired and thus free themselves from the concept of images. They do not gather information with a fixed framerate but instead each pixel is captured asynchronously when needed. These bio-inspired cameras, also called event-based cameras or neuromorphic cameras, therefore have an extremely sparse output and enable, with appropriate algorithms, a real time treatment of the information at an equivalent frequency of a kHz or more. But since the data coming from the sensor are quite different from the images traditionally used in standard vision, Prophesee is also advancing the algorithmic and machine learning side of this new kind of machine vision. This enables our clients to build new applications in a wide range of domains, including industrial automation, connected devices, autonomous vehicles, augmented and virtual reality, and more.

INTERNSHIP POSITION DESCRIPTION

We are looking for passionate interns who demonstrate initiative, take ownership for project work, and exhibit a strong spirit of innovation. The ideal candidate is a curious and creative individual keen on problem-solving and with prior experience in C++ / Python programming and exposure to the Computer Vision / Image Processing / Artificial Intelligence / Machine Learning domains.

She/He will work in a mixed team of scientists and engineers to design, develop & optimize solutions to research problems. Her/His main contribution will be in creating innovative bio-inspired computer vision algorithms for specific tasks across many applications such as computational imaging, 3d sensing, robotics, localization, factory automation, smart devices, aerospace & defense, automotive, etc.

The main required skills common to MOST internship positions are the following (but not exclusively):

Excellent programming skills in C++ and/or in Python Engineering background in Computer Science, Mathematics or related field Prior experience in projects involving at least one of the following domains: Algorithmic design, 3d vision, machine learning, numerical optimization, etc Software development, implementation, architecture, optimization, testing, porting on embedded platforms, etc Development operations, source code versioning, continuous integration & deployment, cloud computing, system administration, etc

R&D

Event Signal Processing versus Spiking Accelerators

Event sensors generate sparse and asynchrounous data which are not compatible with Van Neuran conventionnal computers: the states or memory of any event driven algorithm is tight with the computing part. To scale with larger pixel arrays, the bandwith of these sensors have increased and thus the same applies with the need of low level filtering close to the pixels. Many hardware accelerators have been proposed in the state of the art to ease the event processing, either using FPGAs or dedicated ASICs.

Convolutionnal neural networks are mostly sparse after few layers, and some hardware accelerators already use this feature to faster computations. However, the input information size and frequency has to be defined and fixed. Spiking Neural Networks (SNN), which could be seen as asynchronous recurrent neural networks, are adding asynchronous time based processing to traditionnal neural networks. This feature makes them suitable for event-based data, and the ability to program SSN accelerators will enable application-specific filtering.

The purpose of this internship is to implement low level event processing using SNN accelerators.

The plan is:

SNN accelerator/processing state of the art analysis: Most of the proposed architectures are not suitable with the bandwith of event sensors. Part of this work has to make sure that an existing accelerator can scale up. Software Imlementation of low-level processing functions to filter the event stream using SNN. The algorithms will be similar to the filters implemented inside the event signal processor of Prophesee sensors or will be adapted from recently published academics works. Hardware implementation using a SNN accelerator from Prophesee partners.

BENEFITS

Salary package : 80% SMIC (9,32 €/h)
Meal vouchers : 9€ / working day (Swile card)
Working hours : 35h / week
Office location : Paris Bastille (74 rue du Faubourg Saint Antoine 75012)
Other benefits : CSE

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