جستجوی استاد راهنمای دانشگاهی

Marco Platznerمشاهده پروفایل

استاد

• Reconfigurable Computing
• Approximate Computing
• Self-Aware Computing
• +۷ مورد دیگر

Marco Platzner is a Professor for Computer Engineering at Paderborn University , Germany. He serves as the Dean of Research for the Faculty of Computer Science, Electrical Engineering and Mathematics and heads the Department of Computer Science. Previously, he held research positions at ETH Zurich, Stanford University, GMD (now Fraunhofer IAIS), and Graz University of Technology. Education: Diploma and PhD in Telematics (Graz University of Technology, 1991 and 1996), Habilitation in Hardware-Software Co-Design (ETH Zurich, 2002) Research Interests focus on reconfigurable computing, approximate computing, self-* computing, and embedded systems. His work addresses hardware security, FPGA design, and sustainable AI in data centers. Current projects include energy-efficient AI through deep neural network approximation for FPGAs (EKI-App) and lifecycle sustainability of socio-technical systems (SAIL). Publication Trends show expertise in FPGA security, approximate circuit synthesis, robotics, and hardware acceleration. Collaborations span robotics (ROS 2 integration), AI (transformer optimization), and cybersecurity (Trojan detection). Scientific Awards: ACM SIGDA Hall of Fame (2020) Significant Paper Award (FPL 2015) Best Paper Awards at IEEE ISVLSI (2024), ARC (2018), IEEE ReConFig (2015), and others Weierstraß Prize for Teaching (2008) Leadership Roles include membership in the board of Paderborn Center for Parallel Computing (PC2) and the Jenny Aloni Centre for Early Career Researchers. He has contributed to EU FP7 FET project EPiCS and German priority programs on embedded systems and organic computing.

• Embedded Machine Learning
• High-Performance Computing
• Computer Architecture
• +۶ مورد دیگر

Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.

• Computer Architecture
• Machine Learning
• Cloud Computing
• +۴ مورد دیگر

Chita R. Das is a Professor at Pennsylvania State University, known for extensive contributions in computer architecture, machine learning, and high-performance computing. Their research focuses on optimizing hardware-software co-design for edge computing, cloud infrastructure, and energy-efficient systems. Key areas include FPGA acceleration, GPU optimization, and serverless computing frameworks. Das collaborates frequently with institutions like AMD and Intel, addressing challenges in parallel computing and distributed systems. Their work bridges theoretical advancements with practical applications in recommendation systems, bioinformatics, and real-time video processing. Research interests span across hardware acceleration techniques, cloud resource management, and sustainable computing. Notable projects include adaptive training frameworks for intermittent power environments and neural-augmented game streaming for mobile platforms. Das's publications often address performance bottlenecks in modern architectures and propose novel solutions for latency and energy efficiency. Recent articles highlight innovations in serverless computing cost optimization, low-bandwidth VR streaming, and FPGA-based bioinformatics tools. Their contributions are characterized by interdisciplinary approaches combining computer architecture with machine learning and embedded systems.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Thomas Wildمشاهده پروفایل

استاد

• Manycore system on chip (SoC) architectures
• Network processor (NPU) architectures
• On-chip communication architectures
• +۳ مورد دیگر

Dr.-Ing. Thomas Wild serves as an Academic Director at the Technical University of Munich (TUM), working within the TUM School of Computation, Information and Technology at the Chair of Integrated Systems. He maintains an active research and teaching role at the institution, with his office located in Building N1 (Theresienstr. 90), Room N2136 in Munich, Germany. Dr. Wild's research focuses on advanced computing architectures, with particular emphasis on manycore system on chip (SoC) architectures, network processor (NPU) architectures, on-chip communication architectures including networks on chip (NoC), and system level design methodologies. His work bridges theoretical research with practical implementation, often exploring design space exploration techniques to optimize system performance. The evolution of his research over two decades demonstrates a consistent focus on improving communication architectures and system-level design for embedded and high-performance computing platforms. His recent publications (2023-2025) reveal a growing integration of machine learning techniques with traditional hardware design, particularly in optimizing power-performance tradeoffs in embedded systems. There's a clear trend toward hardware-software co-design approaches, with significant work on SmartNICs, Linux system optimization, and network processing acceleration. His research shows strong interdisciplinary connections between computer architecture, networking, and machine learning. EUROPRACTICE representative for TUM city campus, facilitating access to commercial EDA tools for academic purposes Active collaborator with Professor Andreas Herkersdorf and other researchers at TUM Focus on practical implementations with FPGA-based prototyping and real system modifications Dr. Wild teaches several hardware design courses including VHDL Lab, SystemC Lab, and HW/SW Codesign, contributing to the education of next-generation computer engineers. His teaching directly complements his research in system design and hardware acceleration, providing students with hands-on experience in cutting-edge technologies.

• Reconfigurable Computing
• FPGA Architectures
• Hardware Acceleration
• +۱ مورد دیگر

Prof. Dr. Andreas Koch leads research on reconfigurable computing at TU Darmstadt, focusing on FPGA-based acceleration, heterogeneous architectures, and hardware/software co-design for database systems and embedded applications.

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +۳ مورد دیگر

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +۷ مورد دیگر

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Kenneth Zickمشاهده پروفایل

استاد پژوهش

• Unconventional computing
• Bio-inspired compute systems
• Ising machines
• +۷ مورد دیگر

Dr. Kenneth Zick is a Research Professor at the University of Southern California's Information Sciences Institute (USC ISI), where he serves as Research Director of Transformational Computing. His work focuses on game-changing computer architectures, hardware, and systems for solving critical government problems, with expertise in unconventional computing, quantum computing, and bio-inspired systems. Ph.D. in Computer Science & Engineering, University of Michigan-Ann Arbor M.S. in Electrical Engineering, University of Texas at Dallas Bachelor's in Electrical Engineering, University of Michigan-Ann Arbor Dr. Zick's research interests span unconventional computing , bio-inspired systems , Ising machines , quantum annealing , FPGA-based solutions , and neuromorphic computing . His group develops hardware-centric algorithm discovery and Cosm, a heuristic algorithm for sparse Ising optimization. Current projects include superconducting digital architectures, analog-digital hybrid computing, and human-AI co-design for breakthrough hardware. His team leverages advanced facilities such as USC ISI's MOSIS 2.0 and the California DREAMS hub in the DoD Microelectronics Commons, with expertise in high-speed I/O, FPGA prototyping, and radiation-hardened systems. He has received a NASA Fellowship for his Ph.D. work and mentored students like Aditi, who won the USC ECE Outstanding Academic Achievement Award.

• Hyperdimensional Computing
• Neuromorphic Hardware
• In-Memory Computing
• +۳ مورد دیگر

Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.

• Security Engineering
• Hardware Security
• Post-Quantum Cryptography
• +۴ مورد دیگر

Prof. Tim Güneysu is a full Professor and Head of the Security Engineering department at the Faculty of Computer Science, Ruhr-Universität Bochum. He serves as Vice Dean for Strategy and Finances (since 2023) and previously as Speaker of the Horst-Görtz Institute for IT-Security (2020-2023). His academic journey includes roles as Associate Professor at the University of Bremen (2015-2017) and Assistant Professor at Ruhr-Universität Bochum (2011-2015). He also holds positions at the German Research Center for Artificial Intelligence (DFKI) and has conducted postdoctoral research at UMass Amherst. His research focuses on Security-by-Design principles, CAD for Security, and countermeasures against physical attacks. He emphasizes efficient cryptographic implementations and system-level hardware security. Key areas include post-quantum cryptography, side-channel resistant designs, and secure embedded systems. He has contributed over 200 publications in top venues, with recent work on FPGA-based cryptographic accelerators, secure hardware extensions (e.g., KeyVisor), and post-quantum algorithms for IoT. His research themes include agile signature acceleration, fault attack mitigation, and hardware-software co-design for security. Notable projects include CONVOLVE (edge-AI security) and QuantumRISC (quantum-safe systems). His work bridges theoretical cryptography with practical hardware implementations, emphasizing real-world security applications.

• Embedded Machine Learning
• Neural Network Hardware Acceleration
• Quantized Neural Networks
• +۳ مورد دیگر

Lukas Einhaus is a Researcher and PhD student in the Embedded Systems department at the University of Duisburg-Essen since April 2020, affiliated with the Intelligent Embedded Systems (IES) research group and contributing to initiatives including Elastic AI and the IoT Garage. His academic background includes: Bachelor of Science from University of Duisburg-Essen, thesis focused on programming abstractions for concurrent embedded systems Master of Science from University of Duisburg-Essen, specializing in distributed and reliable systems with thesis research on quantizing neural networks Einhaus's research centers on designing neural networks for efficient hardware implementation on FPGAs, with primary expertise in quantized or low-precision neural networks that reduce bit depth (typically 1-3 bits) for computations and information flow. This work enables energy-efficient AI solutions for embedded and IoT devices where resource constraints are critical. His publication record from 2021-2025 reveals consistent innovation in FPGA-based neural network optimization, with applications spanning fluid flow estimation, time-series analysis, and real-time stream processing. Core themes include Elastic AI for adaptive systems, precomputation techniques for convolutional layers, and hardware-aware neural architecture design. He previously contributed to the BMBF-funded project "KI-Sprung: LUTNet" (until March 2022), developing energy-efficient AI networks using elementary lookup tables for FPGA deployment. Einhaus actively mentors students through the IoT Garage initiative, supervising practical projects including drink-mixing machines, exoskeletons, and ball-challenge systems.

• Embedded Systems
• Computer Engineering
• Electronics
• +۶ مورد دیگر

Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.

Marc Reichenbachمشاهده پروفایل

استاد

• Integrated Circuits
• Embedded Systems
• VLSI Design
• +۷ مورد دیگر

Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.