Andy Pimentel

University of Amsterdam, Institute of Informatics, Faculty Member

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Andy Pimentel is full professor and chairs the Parallel Computing Systems group at the Informatics Institute of the University of Amsterdam. He holds the MSc ('93) and PhD ('98) degrees in computer science, both from the University of Amsterdam. He is co-founder of the International Conference on embedded computer Systems: Architectures, Modeling, and Simulation (SAMOS) and is member of the European Network of Excellence on High-Performance Embedded Architecture and Compilation (HiPEAC). His research centers around system-level modeling, simulation and exploration of multi- and many-core embedded computer systems with the purpose of effectively designing, programming and (runtime) managing these systems. More specifically, the research focus is on the development of techniques, algorithms and tools for the analysis and optimization of so-called extra-functional system behavior, like system performance, power/energy consumption, reliability, cost, etc. Andy Pimentel is associate editor of Elsevier's Simulation Modelling Practice and Theory and Springer's Journal of Signal Processing Systems. He has also been guest associate editor of ACM Transactions on Embedded Computing Systems (2009 and 2010), Transactions on HiPEAC (2010), Journal of Systems Architecture (2007 and 2008), and Journal of Signal Processing Systems (2006 and 2010). Moreover, he serves (or has served) on the organizational committees of basically all leading (embedded) computer systems design conferences and workshops, such as IEEE/ACM DAC (TPC member), DATE (Track Chair of Design, Methods and Tools track, member of DATE Executive Committee, Topic TPC Chair, TPC member), IEEE ICCD (Topic TPC Co-chair), IEEE/ACM CODES ISSS (Vice Program Chair in '16, Program Chair in '17, TPC member), IEEE/ACM ICCAD (TPC member), FPL (Local Organization Chair in '07, TPC member), SAMOS (Co-founder, SC member, General Chair in '05 and '11, TPC Chair in '04), IEEE ESTIMedia (TPC Chair in '08 and '09, General Chair in '10, TPC member), and ACM SAC (TPC member). He is Senior member of the IEEE, member of the IEEE Computer Society, and select member of IFIP Working Group 10.3.

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Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge

ACM Transactions on Embedded Computing Systems, 2022

Convolutional Neural Networks (CNNs) are biologically inspired computational models that are at t... more Convolutional Neural Networks (CNNs) are biologically inspired computational models that are at the heart of many modern computer vision and natural language processing applications. Some of the CNN-based applications are executed on mobile and embedded devices. Execution of CNNs on such devices places numerous demands on the CNNs, such as high accuracy, high throughput, low memory cost, and low energy consumption. These requirements are very difficult to satisfy at the same time, so CNN execution at the edge typically involves trade-offs (e.g., high CNN throughput is achieved at the cost of decreased CNN accuracy). In existing methodologies, such trade-offs are either chosen once and remain unchanged during a CNN-based application execution, or are adapted to the properties of the CNN input data. However, the application needs can also be significantly affected by the changes in the application environment, such as a change of the battery level in the edge device. Thus, CNN-based a...

Andy Pimentel

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