AI AT THE EDGE

FPGAs (24th Jan - 28th Jan 2022)

Overview of Ai at the Edge FPGAs

This workshop will focus on deploying AI models on FPGA boards. ML on chip or AI at the Edge is an emerging field and skilled people in this domain are highly sought-after in the tech industry. Join us for this 5 day intensive workshop to develop this high in-demand skill.

WHY AI AT THE EDGE

Learning Outcomes

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Quantized training & implementation of NNs (major)

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Edge device landscape – Holistic Overview

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Why FPGAs are suitable as accelerators for Neural Networks?

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Quantized training and implementation of NNs

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Real-time inference concepts

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Hands on experience with Xilinx development tools

Topics of the Workshop include, but are not limited to

– Introduction to Machine and Deep Learning
– Overview of AI at the Edge/ML on Chip
– Vivado HLS basics
– Breivats implementation

Save the Date

We Will Provide

All resource materials used in the commencement of this workshop will be provided to the participants.

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Dataset

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Lecture Slides

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Resource Materials

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Code

Important

Pre Requisites for the Workshop Participants

Python – pytorch
Basic understanding of Deep learning models
Basic background in computer architecture like pipelining, caching, instruction-set, assembly programming (for advanced workshops on FPGAs)
Basic understanding of C/C++
Basic understanding of Xilinx FPGAs (optional)

Speakers

Assistant Professor, SEECS-NUST

Dr. Abid Rafique

BIO

Dr. Abid has 19 years of experience in leading teams in public R&D organization on signal processing algorithms, design of FPGA-based high performance computing platforms and hardware acceleration of critical algorithms. He has co-founded multiple startups including Untangle Solutions, Galaxonic and AuditXPRT Technologies with recent exit from AuditXPRT worth 6M GBP. He has a unique skill set of building meaningful business relationships and high performance teams. He holds an MS in Embedded Systems from TU Munich, Germany and a PhD in High Performance Reconfigurable Computing from Imperial College London,UK.

Assistant Professor, LUMS

Dr. Adeel Pasha

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BIO

Dr. Muhammad Adeel Pasha from SBASSE, LUMS, Lahore. Dr. Adeel Pasha works in the area of low-power microarchitecture, energy-efficient hardware design, hardware acceleration of machine learning and deep neural networks, and green computing. He is an Associate Professor at the EE Department, LUMS where he is also serving as the Director of Electronics and Embedded Systems Lab (EESL) since 2014. He is also contributing to the NECOP Fellowship program on Digital IC Design being hosted at LUMS.

Asst. Prof. University of Jeddah - KSA

Dr. Hassan Aqeel Khan

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BIO

Dr. Hassan Aqeel Khan has a PhD in Electrical Engineering from Michigan State University, USA. He is currently working as an Assistant Professor in the Kingdom of Saudi Arabia. He worked as an Assistant Professor at NUST SEECS, Islamabad, Pakistan from November 2015 to December 2019. He has multiple years of teaching and research experience in AI and Machine Learning and a number of his former students are now working as Data Scientists and AI professionals or pursuing graduate studies at leading international Universities. Dr. Khan’s research is primarily focused on Medical Imaging and Biomedical Engineering applications of AI and Computer Vision. He is a founding member of AI Lounge.