Andrew Boutros
PhD Student at the University of Toronto
About me
I am a PhD student in the Department of Electrical and Computer Engineering at the University of Toronto under the supervision of Prof. Vaughn Betz. My research interests are the intersection of FPGA architecture/CAD and AI acceleration. I am a post-graduate affiliate of the Intel/VMware Crossroads 3D-FPGA Academic Research Center, the Vector Institute for Artificial Intelligence, and the International Centre for Spatial Computational Learning. I am also a machine learning systems engineer at MangoBoost. From 2018 to 2022, I was a research scientist at Intel Labs and Intel's Programmable Solutions Group CTO Office. I received my MASc in Computer Engineering from the University of Toronto in 2018, and my BSc in Electronics Engineering from the German University in Cairo in 2016.
News
Oct 19, 2023: Our work on 3D-stacked reconfigurable acceleration devices is accepted for publication in FPT'23!
May 24, 2023: Our work on the architecture exploration flow of future RADs is accepted for publication in FPL'23!
Apr 30, 2023: Our book chapter on FPGA architecture is published as part of the Handbook of Computer Architecture by Springer Nature!
Apr 20, 2023: Our work extending the Koios suite of deep learning FPGA benchmark circuits is accepted for publication in TCAD!
Mar 17, 2023: Our work on placement optimization for FPGAs with embedded hard NoCs is accepted for publication as a full paper in FCCM'23!
Sep 5, 2022: Our work on flexible FPGA-based acceleration of NLP models (BERT, GPT) is accepted for publication in TACO!
Aug 29, 2022: Our journal paper on architecture and application co-design for new beyond-FPGA devices is accepted for publication in IEEE Access!
July 3, 2022: Our paper on FPGA smart NICs for AI training is accepted for publication in the IEEE Computer Architecture Letters!
June 14, 2022: Our paper on architecture exploration for novel beyond-FPGA reconfigurable acceleration devices is accepted for publication in FPL'22!
Dec 22, 2021: Our paper on specializing AI FPGA overlays won one of the best paper awards in ICM'21!
Oct 17, 2021: Our work on specializing AI overlays for target workloads is accepted for publication in ICM'21!
Aug 6, 2021: I will be giving a talk as part of the Open-Source FPGA Foundation seminar series on FPGAs and deep learning. Sign up for it here!
May 25, 2021: Our survey on the principles and progression of FPGA architecture is published in the IEEE Circuits and Systems magazine!
May 14, 2021: Two full papers accepted for publication in FPL'21!
April 13, 2021: Our work on the Stratix 10 NX neural processing unit is featured on Intel's Stratix 10 NX official webpage and in this white paper.
March 8, 2021: Our work on enhancing FPGAs with in-BRAM compute for deep learning was accepted for publication as a full paper in FCCM'21!
Dec 9, 2020: Our paper on deep learning security in multi-tenant cloud FPGAs was nominated for the best paper award in FPT'20!
Nov 1, 2020: Two full papers accepted for publication in FPT’20! Too bad I cannot visit Hawaii during the COVID-19 pandemic :(
Apr 10, 2020: Our work on optimizing FPGA logic blocks for deep learning arithmetic was accepted for publication in TRETS.
Mar 24, 2020: I am honored to join the Vector Institute as one of 22 post-graduate affiliates across Canada in the 2020 cohort (Announcement).
Oct 6, 2019: Our work on multi-FPGA acceleration of neural machine translation acceleration was accepted for publication in FPT’19!
Mar 3, 2019: Our work on FPGA and ASIC integration for persistent RNNs was accepted for publication in FCCM’19.
Nov 15, 2018: Our work on FPGA logic blocks for low-precision deep learning was accepted for publication in FPGA’19.
Nov 15, 2018: Our work on evaluating and enhancing Intel Stratix 10 FPGAs for persistent AI was accepted for a poster presentation in FPGA’19.
Aug 31, 2018: Our paper on low-precision DSP blocks for deep learning won the S. Vassiliadis Best Paper Award in FPL’18!
Aug 8, 2018: I successfully defended my MASc thesis titled “Enhancing FPGA Architecture for Efficient Deep Learning Inference”!
Jul 25, 2018: Our work on quantifying the efficiency gap between FPGA and ASIC CNN accelerators was accepted for publication in TRETS.
Apr 21, 2018: I won the Right Track CAD Graduate Scholarship for 2017-18.
May 21, 2018: Our work on low-precision DSP blocks for deep learning was accepted for publication in FPL’18.
Feb 1, 2018: I am thrilled to join the Vector Institute as a post-graduate affiliate in the 2018 cohort (Announcement).