Ahmet Alper Goksoy
Graduate Research Assistant at University of Wisconsin-Madison
Graduate Research Assistant at University of Wisconsin-Madison
Domain Specific SoCs, Task Scheduling, Graph Neural Networks, Reinforcement Learning
University of Wisconsin-Madison
https://www.linkedin.com/company/uwmadison
- Developed a zero-overhead, energy-efficient task scheduling (DAS) framework that combines benefits of a fast, low-overhead scheduler and a high-performance, high-overhead scheduler for heterogeneous SoCs.
- Performed a detailed theoretical analysis of DAS framework and its validation on a system level simulator.
- Developed Graph Neural Network (GNN) and Deep Neural Network (DNN) based Imitation Learning (IL) and Reinforcement Learning (RL) task schedulers for domain-specific SoCs.
- Contributed to an in-memory computing-based GNN hardware accelerator design.
- Developing sparsity eliminator algorithms for a communication-aware GNN hardware accelerator design. 
- Developing hardware-aware neural architecture search (NAS) algorithms for sparse neural networks.
- Contributed to the development of a system-level domain-specific SoC simulation framework.
Madison, Wisconsin, United States
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Graduate Research Assistant
Arizona State University
https://www.linkedin.com/company/arizona-state-university
- Contributed to the development of an IL-based task scheduling framework for heterogeneous SoCs.
- Contributed to the development of a system-level domain-specific SoC simulation framework.
Phoenix, Arizona Area
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Graduate Research Assistant
Mikroelektronik Ltd. (MKR-IC)
https://www.linkedin.com/company/mikroelektronik-ltd-
Designed a single-ended 8 bit 250 MSPS SAR ADC.
Istanbul, Turkey
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Intern
Aselsan
https://www.linkedin.com/company/aselsan
Contributed to the development of RTOS for an SoC board using VxWorks.
Ankara, Turkey
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Intern
Arcelik Group
https://www.linkedin.com/company/arcelikglobal
Designing test programs for the production line and fixing bugs on TVs. Designing test programs for experiments to decrease the time of the White Colour Balance Process.
İstanbul, Türkiye
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R&D Software Applications Intern
BUEC - Boğaziçi IEEE Student Branch
https://www.linkedin.com/company/buec---bo%C4%9Fazi%C3%A7i-ieee-student-branch
İstanbul, Türkiye
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Member
Doctor of Philosophy - PhD
Transferred from ASU
Electrical Engineering
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University of Wisconsin-Madison
https://www.linkedin.com/school/uwmadison/
Doctor of Philosophy - PhD
Electrical Engineering
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Arizona State University
https://www.linkedin.com/school/arizona-state-university/
Bachelor's degree
Electrical and Electronics Engineering
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Boğaziçi University
https://www.linkedin.com/school/bogazici-university/
Bachelor of Arts - BA
Online Education Program
Sociology
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Istanbul University
https://www.linkedin.com/school/istanbul-university/
Isparta Süleyman Demirel Fen Lisesi
Domain-specific systems-on-chip (DSSoCs) aim at bridging the gap between application-specific integrated circuits (ASICs) and general-purpose processors. Traditional operating system (OS) schedulers can undermine the potential of DSSoCs since their execution times can be orders of magnitude larger than the execution time of the task itself. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the benefits of a fast (low-overhead) scheduler and a slow (sophisticated, high-performance but high-overhead) scheduler. Experiments with five real-world streaming applications show that DAS consistently outperforms both the fast and slow schedulers. For 40 different workloads, DAS achieves on average 1.29× speedup and 45% lower EDP compared to the sophisticated scheduler at low data rates and 1.28× speedup and 37% lower EDP than the fast scheduler when the workload complexity increases
DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs
IEEE
https://ieeexplore.ieee.org/iel7/4563995/5170179/09530260.pdf
Domain-specific systems-on-chip, a class of heterogeneous many-core systems, are recognized as a key
approach to narrow down the performance and energy-efficiency gap between custom hardware accelerators and programmable processors. Reaching the full potential of these architectures
depends critically on optimally scheduling the applications to available resources at runtime. Existing optimization-based techniques cannot achieve this objective at runtime due to the combinatorial nature of the task scheduling problem. As the main theoretical contribution, this paper poses scheduling
as a classification problem and proposes a hierarchical imitation learning (IL)-based scheduler that learns from an Oracle to maximize the performance of multiple domain-specific applications. Extensive evaluations with six streaming applications from wireless communications and radar domains
show that the proposed IL-based scheduler approximates an offline Oracle policy with more than 99% accuracy for performance- and energy-based optimization objectives.  Furthermore, it achieves almost identical performance to the Oracle with a low runtime overhead and successfully adapts to
new applications, many-core system configurations, and runtime variations in application characteristics
Runtime task scheduling using imitation learning for heterogeneous many-core systems
IEEE
https://arxiv.org/pdf/2007.09361.pdf
Heterogeneous systems-on-chip (SoCs) are highly favorable computing platforms due to their superior performance and energy efficiency potential compared to homogeneous architectures. They can be further tailored to a specific domain of applications by incorporating processing elements (PEs) that accelerate frequently used kernels in these applications. However, this potential is contingent upon optimizing the SoC for the target domain and utilizing its resources effectively at runtime. To this end, system-level design - including scheduling, power-thermal management algorithms, and design space exploration studies - plays a crucial role. This article presents a system-level domain-specific SoC simulation (DS3) framework to address this need. DS3 enables both design space exploration and dynamic resource management for power-performance optimization of domain applications. We showcase DS3 using six real-world applications from wireless communications and radar processing domain. DS3, as well as the reference applications, is shared as open-source software to stimulate research in this area
DS3: A System-Level Domain-Specific System-on-Chip Simulation Framework
IEEE
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9072509
A simulation framework for domain-specific system-on-chips: work-in-progress
ACM
https://dl.acm.org/doi/pdf/10.1145/3349567.3351719
I received a full free registration to DAC 2021 and worked on a hands-on lab experience where we designed a NN accelerator using a High Level Synthesis tool.
Design Automation Conference (DAC)
DAC 2021 Young Fellowship
I received a full free DAC (Design Automation Conference) registration, along with a travel award that covers travel, lodging, and incidentals.
Design Automation Conference (DAC)
Richard Newton Young Fellow
Advanced Computer Architecture I
ECE 752
CAD Tools in VLSI
CS 756
Introduction to Computer Architecture
ECE 552
Computational Image Understanding and Pattern Analysis
EEE 598
Minds and Machines
AME 598
Reinforcement Learning
CSE 691
Statistical Machine Learning
EEE 598
System Level Design for Multicore Architectures
EEE 598
VLSI Design
EEE 525
Cadence Design Systems
www.credly.com
High Level Synthesis Tutorial - DAC 2021
https://www.credly.com/badges/56c676ed-8ec9-4fe7-a895-794939bc7102?source=linked_in_profile
https://tr.linkedin.com/in/g%C3%B6n%C3%BCl-%C5%9Fakar-082542162
Turkey
Gönül Şakar
Software Engineer at Turkish Aerospace
https://tr.linkedin.com/in/gokcan-tatli-298380a0
Istanbul
Gokcan Tatli
PhD Student at UW-Madison
https://www.linkedin.com/in/safamedin
Cambridge, MA
Safa C. Medin
Graduate Research Assistant at Massachusetts Institute of Technology
https://ch.linkedin.com/in/ozan-yakar-85077a112
Lausanne Metropolitan Area
Ozan Yakar
Doctoral Student at EPFL (École polytechnique fédérale de Lausanne)
https://tr.linkedin.com/in/%C3%A7a%C4%9Fatay-ate%C5%9F-484439134
Besiktas
Çağatay Ateş
Boğaziçi Üniversitesi şirketinde Research Assistant
https://tr.linkedin.com/in/iremsireli
Ankara, Turkey
İrem Şireli
Software Developer at IOTIQ GmbH
https://tr.linkedin.com/in/ismail-rasim-%C3%BClgen-8566789b
Turkey
İsmail Rasim Ülgen
R&D Engineer at Sestek
https://de.linkedin.com/in/deniz-tomar-603441134
Munich
Deniz Tomar
Graduate Student at Technical University of Munich | Communications Engineering
https://fr.linkedin.com/in/%C3%A7a%C4%9Fla-okyay-5180591ba
Évry
Çağla Okyay
PhD Candidate in Physics at Université Paris-Saclay
https://www.linkedin.com/in/bengisucagiltay
Madison, WI
Bengisu Cagiltay
Graduate Research Assistant at University of Wisconsin-Madison
https://www.linkedin.com/in/u%C4%9Fur-cin-1114a020a
Madison, WI
Uğur Cin
Ph.D. Student in Mathematics
https://tr.linkedin.com/in/umut-mi%C5%9Fe-b78030202
Istanbul, Turkey
Umut Mişe
PhD Candidate - Boğaziçi Üniversitesi
https://www.linkedin.com/in/sri-subramanian
Madison, WI
Srinath Subramanian
Graduate Research Assistant at University of Wisconsin-Madison
https://www.linkedin.com/in/tuo-wang-0485b5133
Madison, WI
Tuo Wang
Ph.D. Student in UW-Madison
https://www.linkedin.com/in/kgodeneli
Berkeley, CA
Kadircan Gödeneli
PhD Student at UC Berkeley
https://tr.linkedin.com/in/burak-sefa-sert-4b1787105
Ankara, Turkey
Burak Sefa SERT
--
https://www.linkedin.com/in/dogadogan
Greater Boston
Mustafa Doga Dogan
PhD Candidate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
https://www.linkedin.com/in/mostafaessawy
Corvallis, OR
Mostafa Essawy
Doctoral Student at Oregon State University
https://www.linkedin.com/in/pooja-guhan
College Park, MD
Pooja Guhan
CS PhD student @ UMD | vGHC'21 | Adobe Research Intern 2021
https://www.linkedin.com/in/gajanan-kothawade-b94b8291
Prosser, WA
Gajanan Kothawade
Graduate Research Assistant | Center for Precision and Automated Agricultural Systems | Dept of Biological Systems Engineering | Washington State University
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Congratulations to Associate Professor Umit Ogras for being named Outstanding Researcher by Intel!Read the full…
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Just presented our work "Control Variate Approximation for DNN Accelerators", a collaboration with the Karlsruhe Institute of Technology and the…
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ECE Associate Professor Umit Ogras and PhD student Sumit Kumar Mandal received the 2021 best paper award from the ACM, Association for Computing…
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Boğaziçi University Students
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