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Sobre
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Sobre

João M. P. Cardoso obteve o grau de Doutor em Engenharia Electrotécnica e Computadores no IST/UTL (Instituto Superior Técnico/Universidade Técnica de Lisboa), Lisboa, Portugal, em 2001. É actualmente Professor Catedrático no Departamento de Engenharia Informática (DEI) da Faculdade de Engenharia da Universidade do Porto (FEUP) e investigador sénior no INESC TEC. Previamente, ele foi Prof. Auxiliar no IST/UTL (2006-2008), investigador sénior no INESC-ID (2001-2009), e Prof. Auxiliar na Universidade do Algarve (1993-2006). Em 2001/2002, trabalhou na PACT XPP Technologies, Inc., em Munique, Alemanha. Tem estado envolvido na organização e tem servido como membro do comité científico de muitas conferências internacionais. Por exemplo, foi General Co-Chair da IEEE/IFIP EUC’2015 e da IEEE CSE’2015, General Chair da FPL’2013, General Co-Chair da ARC’2014 e ARC’2006, Program Co-Chair da ARCS’2016, DASIP’2014, e RAW’2010. É co-autor de mais de 150 publicações científicas em tópicos relacionados com compiladores, sistemas embebidos, e computação reconfigurável. Coordenou vários projectos de investigação. É um membro sénior do IEEE e do ACM e membro da IEEE Computer Society. Os seus interesses de investigação incluem técnicas de compiladores, linguages específicas ao domínio, computação reconfigurável, arquitecturas específicas à aplicação, e computação de elevado desempenho com ênfase em computação embebida.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Paiva Cardoso
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 julho 2011
003
Publicações

2021

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

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

Publicação
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

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

Publicação
Neurocomputing

Abstract

2021

Formal verification of Matrix based MATLAB models using interactive theorem proving

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

Publicação
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

Autores
Pinto, P; Cardoso, JMP;

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

Abstract

2021

A Binary Translation Framework for Automated Hardware Generation

Autores
Paulino, N; Bispo, J; Ferreira, JC; Cardoso, JMP;

Publicação
IEEE Micro

Abstract

Teses
supervisionadas

2020

Restructuring C code for High-Level Synthesis Targeting FPGAs

Autor
Renato Alexandre Sousa Campos

Instituição
UP-FEUP

2020

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

Autor
Luis Alexandre Cubal dos Reis

Instituição
UP-FEUP

2020

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

Autor
Tiago Lascasas dos Santos

Instituição
UP-FEUP

2020

Runtime-aware Compiler Optimizations for High-Performance Embedded Computing

Autor
Pedro Miguel dos Santos Pinto

Instituição
UP-FEUP

2020

Scalable and Configurable Event Processing Engine

Autor
Edgar de Lemos Passos

Instituição
UP-FEUP