Virtual Meetup - Mar 3, 2022

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https://zoom.us/webinar/register/WN_ZstwxUvsQd-M_Lz0nAV2og

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• Welcome, Announcements & Housekeeping
• Talk #1: Introduction to Kubeflow - 10 mins
• Talk #2: "Deep Learning in Robotic Vision - A Confluence of Architectures"

The confluence of Deep Learning being applied to Robotics is nexus of rapidly evolving technologies, both for industrial automation as well as field robotics. This talk gives us an introduction into the technological opportunities, and the architectural challenges that come with operating in the crossroads of these fields as they transition from labs and universities and into scalable real-world applications.

Speaker Kausthub Krishnamurthy is a Machine Learning & Robotics Engineer, with a focus on Computer Vision and Sensing Technologies. In both industrial robotics and mobile robotic environments, he has applied deep learning solutions to create proof of concept models, and is currently looking at leveraging kubeflow to explore scalable deployed Deep Learning solutions.

• Talk #3: Porting Signal processing algorithms to CuPy for precision measurement

At European Organization for Nuclear Research(CERN), for the alignment of large superconducting magnets and cryogenics, an interferometry based system is being devised to identify the position of their elements. This technique uses interferometry principle and uses sweeping laser to
identify the distance of multiple points using Fourier Analysis. The
data acquired from photo-detection module, received after a sweep of
laser source, needs to undergo sophisticated post processing to obtain
the final results. The system must monitor position of a large number of
elements every second. Dealing with 1000s of target points in less than
1 second required time-optimized and precise calculation. Thus, GPU was
employed to provide faster and precise results. This required to use
signal processing algorithms like: Butterworth Filter, Hilbert
Transform, Savitzky-Golay smoothing Filter in GPU. This talk will cover
steps involved in adopting signal processing algorithm to GPU to achieve
better performance. CuPy provides wrapper for most of the CUDA toolkit
in Python. This talk will also highlight about performance metrics with
respect to increase in the data size and possible optimizations of its
processing.

Mamta Shukla is a software engineer (fellow) at CERN, developing Linux device drivers and libraries for in-house electronics. Graduated as
electronics and telecommunication engineer from India in 2018. She
started her journey in tech as an Outreachy Intern and worked with
Linux GPU subsystem and contributed to VKMS driver. She is an open source enthusiast and loves to contribute and promote FOSS.
At CERN, worked on Frequency Scanning Interferometry (FSI) system which will be used for alignment of Cryogenics for High-Luminosiy LHC. While working for FSI, with experience of GPU subsystem - developed and ported signal processing algortihms on GPU using CuPy.