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About

João M. P. Cardoso received his PhD degree in Electrical and Computer Engineering from the IST/UTL (Technical University of Lisbon), Lisbon, Portugal in 2001. He is currently Full Professor at the Department of Informatics Eng., Faculty of Eng. of the University of Porto, Porto, Portugal, and a research member of INESC TEC. Before, he was with the IST/UTL (2006-2008), a senior researcher at INESC-ID (2001-2009), and with the University of Algarve (1993-2006). In 2001/2002, he worked for PACT XPP Technologies, Inc., Munich, Germany. He has been involved in the organization and served as a Program Committee member for many international conferences. For example, he was general Co-Chair of IEEE/IFIP EUC’2015 and IEEE CSE’2015, General Chair of FPL’2013, General Co-Chair of ARC’2014 and ARC’2006, Program Co-Chair of ARCS’2016, DASIP’2014, and RAW’2010. He has (co-)authored over 150 scientific publications on subjects related to compilers, embedded systems, and reconfigurable computing. He has coordinated a number of research projects. He is a senior member of IEEE, a member of IEEE Computer Society, and a senior member of ACM.  His research interests include compilation techniques, domain-specific languages, reconfigurable computing, application-specific architectures, and high-performance computing with a particular emphasis in embedded computing.

Interest
Topics
Details

Details

  • Name

    João Paiva Cardoso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st July 2011
003
Publications

2021

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Authors
Vitali, E; Gadioli, D; Palermo, G; Golasowski, M; Bispo, J; Pinto, P; Martinovic, J; Slaninova, K; Cardoso, JMP; Silvano, C;

Publication
IEEE Transactions on Emerging Topics in Computing

Abstract
Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. The corresponding speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline. OAPA

2021

An Ensemble of Autonomous Auto-Encoders for Human Activity Recognition

Authors
Garcia, KD; de Sa, CR; Poel, M; Carvalho, T; Mendes Moreira, J; Cardoso, JMP; de Carvalho, ACPLF; Kok, JN;

Publication
Neurocomputing

Abstract

2021

Formal verification of Matrix based MATLAB models using interactive theorem proving

Authors
Gauhar, A; Rashid, A; Hasan, O; Bispo, J; Cardoso, JMP;

Publication
PeerJ Computer Science

Abstract
MATLAB is a software based analysis environment that supports a high-level programing language and is widely used to model and analyze systems in various domains of engineering and sciences. Traditionally, the analysis of MATLAB models is done using simulation and debugging/testing frameworks. These methods provide limited coverage due to their inherent incompleteness. Formal verification can overcome these limitations, but developing the formal models of the underlying MATLAB models is a very challenging and time-consuming task, especially in the case of higher-order-logic models. To facilitate this process, we present a library of higher-order-logic functions corresponding to the commonly used matrix functions of MATLAB as well as a translator that allows automatic conversion of MATLAB models to higher-order logic. The formal models can then be formally verified in an interactive theorem prover. For illustrating the usefulness of the proposed library and approach, we present the formal analysis of a Finite Impulse Response (FIR) filter, which is quite commonly used in digital signal processing applications, within the sound core of the HOL Light theorem prover. Copyright 2021 Gauhar et al.

2021

A methodology and framework for software memoization of functions

Authors
Pinto, P; Cardoso, JMP;

Publication
CF '21: Computing Frontiers Conference, Virtual Event, Italy, May 11-13, 2021

Abstract

2021

On Data Parallelism Code Restructuring for HLS Targeting FPGAs

Authors
Campos, R; Cardoso, JMP;

Publication
IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops 2021, Portland, OR, USA, June 17-21, 2021

Abstract

Supervised
thesis

2020

Runtime-aware Compiler Optimizations for High-Performance Embedded Computing

Author
Pedro Miguel dos Santos Pinto

Institution
UP-FEUP

2020

Scalable and Configurable Event Processing Engine

Author
Edgar de Lemos Passos

Institution
UP-FEUP

2020

Restructuring C code for High-Level Synthesis Targeting FPGAs

Author
Renato Alexandre Sousa Campos

Institution
UP-FEUP

2020

Multitarget Compilation Techniques for Generating Efficient OpenCL Code from Matrix-Oriented Computations

Author
Luis Alexandre Cubal dos Reis

Institution
UP-FEUP

2020

Acceleration of Applications with FPGA-based Computing Machines: Code Restructuring

Author
Tiago Lascasas dos Santos

Institution
UP-FEUP