Phd Position at Ietr
il y a 3 semaines
**About us**:
INSA Rennes, a founding member of the INSA Group, is the largest public engineering school in Brittany.
It welcomes 2,200 students and apprentices and graduates more than 340 engineers, 60 masters and 40 PhDs per year.
Composed of 10 teaching departments, including 7 specialties and an apprenticeship program, and supervised by 6 research laboratories, INSA employs approximately 500 public employees (research professors, teachers, permanent and contractual employees) and more than 400 temporary employees, particularly from companies.
**Mission**:
Like all industrial sectors, the electronics sector must, to be sustainable, drastically reduce its CO2 emissions by 2050, and more globally commit to a logic of circular economy. While environmental impacts of electronics are increasingly understood, the evaluation of impacts in Life Cycle Assessments (LCA) is still an emerging practice that requires complex models, data and insights. Once the impacts are known, ecodesign strategies must be developed and assessed.
The impacts of an electronic system arise from its manufacturing, use and end-of-life. The system itself is composed of printed circuit boards (PCB), semiconductor integrated circuits (ICs), passive components, connectors, sensors, actuators, batteries and displays, each having environmental impacts. In particular, the greenhouse gas (GHG) emissions of semiconductor manufacturing represent an important share of the embodied carbon footprint of electronics systems and must be considered alongside energy consumption and end-of-life [M21][P22][U22].
A smart vision system is a combination of sensors and digital processing that captures, preprocesses and distributes a video or a semantic description of a visual scene. It can take the form of a smart camera, mono-sensor [B14] or multi-sensors [K07], or of a camera module in an embedded system comprising a sophisticated image processing pipeline [M20]. It can also comprise a set of distributed sensors [R10]. Current smart vision systems embed optics, Complementary Metal-Oxide-Semiconductor (CMOS) sensors, color processing, image enhancement, video compression and artificial intelligence for image and video analysis. The sophistication of these systems is increasing, as examplified in smartphones that combine several sensors, dedicated Image Signal Processors (ISPs) and software post-processing to implement computational photography [D21].
This thesis will explore ecodesign of smart vision systems as representative examples of high performance embedded systems. The carbon impact of such systems is currently dominated by the embodied emissions, i.e. the greenhouse gas emissions of system fabrication [G21], but results from a complex and difficult to evaluate combination of fabrication, use and end-of-life impacts of its components and software layers [G21] as illustrated in Figure 1. The thesis will aim at understanding how to model linear and non-linear factors influencing carbon emissions of such a system and developing ecodesign methods to minimize environmental impacts by leveraging on circular economy.
**References**
[B14] Birem, M., & Berry, F. (2014). DreamCam: A modular FPGA-based smart camera architecture. Journal of Systems Architecture, 60(6), 519-527.
[D21] Delbracio, M., Kelly, D., Brown, M. S., & Milanfar, P. (2021). Mobile computational photography: A tour. Annual Review of Vision Science, 7, 571-604.
[G21] Gupta, U., Kim, Y. G., Lee, S., Tse, J., Lee, H. H. S., Wei, G. Y.,& Wu, C. J. (2021, February). Chasing carbon: The elusive environmental footprint of computing. In 2021 IEEE International Symposium on High
- Performance Computer Architecture (HPCA) (pp. 854-867). IEEE.
[K06] King, A. M., Burgess, S. C., Ijomah, W., & McMahon, C. A. (2006). Reducing waste: repair, recondition, remanufacture or recycle?. Sustainable development, 14(4), 257-267.
[K07] Klausner, A., Tengg, A., & Rinner, B. (2007, September). Vehicle classification on multi-sensor smart cameras using feature-and decision-fusion. In 2007 First ACM/IEEE International Conference on Distributed Smart Cameras (pp. 67-74). IEEE.
[M20] Mosleh, A., Sharma, A., Onzon, E., Mannan, F., Robidoux, N., & Heide, F. (2020). Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7529-7538).
[M21] Moreau, N., Pirson, T., Le Brun, G., Delhaye, T., Sandu, G., Paris, A.,& Raskin, J. P. (2021). Could unsustainable electronics support sustainability?. Sustainability, 13(12), 6541.
[P22] Pirson, T., Delhaye, T. P., Pip, A., Le Brun, G., Raskin, J. P., & Bol, D. (2022). The environmental footprint of ic production: Review, analysis and lessons from historical trends. IEEE Transactions on Semiconductor Manufacturing.
[R10] Real, F., Berry, F., & Shi, Y. (2010). Smart Cameras: Fundamentals, Technologies and Applications. Smart Cameras, Springer, 10, 978-1.
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