Intelligent systems engineering with reconfigurable computing

Intelligent computing systems comprising microprocessor cores, memory and reconfigurable user-programmable logic represent a promising technology which is well-suited for applications such as digital signal and image processing, cryptography and encryption, etc. These applications employ frequently recursive algorithms which are particularly appropriate when the underlying problem is defined in recursive terms and it is difficult to reformulate it as an iterative procedure. It is known, however, that hardware description languages (such as VHDL) as well as system-level specification languages (such as Handel-C) that are usually employed for specifying the required functionality of reconfigurable systems do not provide a direct support for recursion. In this paper a method allowing recursive algorithms to be easily described in Handel-C and implemented in an FPGA (field-programmable gate array) is proposed. The recursive search algorithm for the knapsack problem is considered as an exampleApplications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

A survey of network-based hardware accelerators

Many practical data-processing algorithms fail to execute efficiently on general-purpose CPUs (Central Processing Units) due to the sequential matter of their operations and memory bandwidth limitations. To achieve desired performance levels, reconfigurable (FPGA (Field-Programmable Gate Array)-based) hardware accelerators are frequently explored that permit the processing units’ architectures to be better adapted to the specific problem/algorithm requirements. In particular, network-based data-processing algorithms are very well suited to implementation in reconfigurable hardware because several data-independent operations can easily and naturally be executed in parallel over as many processing blocks as actually required and technically possible. GPUs (Graphics Processing Units) have also demonstrated good results in this area but they tend to use significantly more power than FPGA, which could be a limiting factor in embedded applications. Moreover, GPUs employ a Single Instruction, Multiple Threads (SIMT) execution model and are therefore optimized to SIMD (Single Instruction, Multiple Data) operations, while in FPGAs fully custom datapaths can be built, eliminating much of the control overhead. This review paper aims to analyze, compare, and discuss different approaches to implementing network-based hardware accelerators in FPGA and programmable SoC (Systems-on-Chip). The performed analysis and the derived recommendations would be useful to hardware designers of future network-based hardware accelerators.publishe

Analysis and comparison of different approaches to implementing a network-based parallel data processing algorithm

It is well known that network-based parallel data processing algorithms are well suited to implementation in reconfigurable hardware recurring to either Field-Programmable Gate Arrays (FPGA) or Programmable Systems-on-Chip (PSoC). The intrinsic parallelism of these devices makes it possible to execute several data-independent network operations in parallel. However, the approaches to designing the respective systems vary significantly with the experience and background of the engineer in charge. In this paper, we analyze and compare the pros and cons of using an embedded processor, high-level synthesis methods, and register-transfer low-level design in terms of design effort, performance, and power consumption for implementing a parallel algorithm to find the two smallest values in a dataset. This problem is easy to formulate, has a number of practical applications (for instance, in low-density parity check decoders), and is very well suited to parallel implementation based on comparator networks.publishe

Arquitecturas reconfiguráveis para problemas de optimização combinatória

Os problemas combinatórios têm uma gama extremamente ampla de aplicações numa variedade de áreas de engenharia, incluindo teste de circuitos electrónicos, reconhecimento de padrões, síntese lógica, etc. Muitos dos problemas de interesse pertencem às classes NP-hard e NP-complete, o que implica que os algoritmos relevantes têm no pior caso complexidade exponencial. Este facto impede a solução de muitos problemas práticos com a ajuda de computadores convencionais. As implementações em circuitos integrados específicos também não são viáveis, em particular por causa da própria heterogeneidade dos problemas combinatórios. Uma solução alternativa consiste no uso de dispositivos reconfiguráveis que podem ser personalizados para um algoritmo específico e reutilizados para outros algoritmos via uma simples reprogramação da sua estrutura interna. As implementações baseadas em hardware reconfigurável permitem optimizar a execução dos algoritmos relevantes com a ajuda de técnicas tais como processamento paralelo, unidades funcionais personalizadas, etc. Tais implementações possibilitam conter o efeito de crescimento exponencial do tempo de computação, permitindo deste modo a solução de problemas combinatórios complexos. Recentemente foram desenvolvidos vários sistemas reconfiguráveis destinados a resolver problemas combinatórios. Estes são principalmente baseados na ideia de hardware específico para a instância, em que para cada instância do problema é gerado um circuito particular. Nesta tese exploramos duas abordagens alternativas. A primeira é orientada para o domínio e permite processar uma variedade de problemas da área da computação combinatória. Para tal é projectado e implementado um processador combinatório reconfigurável e são desenvolvidos métodos e ferramentas que asseguram a sua reconfiguração dinâmica parcial. A segunda abordagem é orientada para a aplicação e é destinada a resolver um problema combinatório específico. Em particular, é proposta uma arquitectura inovadora para a solução do problema de satisfação booleana com a ajuda de uma combinação de software e de hardware reconfigurável. A técnica adoptada elimina a compilação de hardware específica à instância e permite processar problemas que excedem os recursos lógicos disponíveis. São também exploradas as possibilidades de implementação em hardware reconfigurável de estratégias evolutivas para o caso do problema do caixeiro viajante. Esta tese estende o domínio de aplicação da computação reconfigurável ao demonstrar que esta é capaz de acelerar algoritmos com fluxos de controlo complexos.Combinatorial problems have an extremely wide range of practical applications in a variety of engineering areas, including the testing of electronic circuits, pattern recognition, logic synthesis, etc. Many of the problems of interest belong to the classes NP-hard and NP-complete, which implies that the relevant algorithms have an exponential worst-case complexity. This fact precludes the solution of many practical problems with conventional computers. ASIC-based implementations are also not viable, in particular because of the inherent heterogeneity of combinatorial problems. Reconfigurable devices offer an alternative solution, which can be customized to the requirements of a specific algorithm and reutilized for other algorithms via a simple reprogramming of their internal structure. Implementations based on reconfigurable hardware permit the execution of the relevant algorithms to be optimized with the aid of such techniques as parallel processing, personalized functional units, etc. Such implementations allow the effect of exponential growth in the computation time to be delayed, thus enabling more complex problem instances to be solved. Recently, a few reconfigurable engines for combinatorial problems have been developed. They are mainly based on the idea of instance-specific hardware, which assumes that a particular circuit is generated for each problem instance. In this thesis we explore two alternative approaches. The first, domain-specific, approach enables a variety of problems in the area of combinatorial computation to be addressed. For this purpose, a reconfigurable combinatorial processor has been designed and implemented and a number of methods and tools that support its partial dynamic reconfiguration have been developed. The second, application-specific, approach is oriented towards solving individual combinatorial problems. In particular, a novel architecture is proposed for solving the Boolean satisfiability problem with the aid of software and reconfigurable hardware. The adopted technique avoids instance-specific hardware compilation and permits problems that exceed the available logic resources to be solved. The possibility of implementing evolutionary strategies for the traveling salesman problem in reconfigurable hardware is also explored. This thesis extends the application domain of reconfigurable computing by demonstrating that it is effective in accelerating algorithms with complex control flows

Intelligent systems engineering with reconfigurable computing

Intelligent computing systems comprising microprocessor cores, memory and reconfigurable user-programmable logic represent a promising technology which is well-suited for applications such as digital signal and image processing, cryptography and encryption, etc. These applications employ frequently recursive algorithms which are particularly appropriate when the underlying problem is defined in recursive terms and it is difficult to reformulate it as an iterative procedure. It is known, however, that hardware description languages (such as VHDL) as well as system-level specification languages (such as Handel-C) that are usually employed for specifying the required functionality of reconfigurable systems do not provide a direct support for recursion. In this paper a method allowing recursive algorithms to be easily described in Handel-C and implemented in an FPGA (field-programmable gate array) is proposed. The recursive search algorithm for the knapsack problem is considered as an exampleApplications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary algorithm for state encoding

This paper presents an encoding technique that is common for many different logic synthesis problems. It enables us to construct a system of Boolean functions, and then to decompose this system into sub-systems in such a way that a dependency of functions, included into each sub-system, on the respective arguments is reduced. For complex applications such type of encoding has a high computational complexity and the paper proposes a novel evolutionary algorithm for the solution of this problemIFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

E-learning tools and web-resources for teaching reconfigurable systems

The paper shows that the teaching of reconfigurable systems is a complex and difficult process requiring many novel methods and tools to be considered within the same course. This process can be greatly simplified with the aid of the information resources and e-learning tools that have been developed and incorporated into an integrated methodology that includes original evaluation and motivation methods. This methodology has been successfully applied to the teaching of reconfigurable systems for approximately 10 years. The paper discusses the impact of this methodology on such important components of higher education as research activity, postgraduate learning, and training for engineering work in industry.Education for the 21 st century - impact of ICT and Digital Resources ConferenceRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary algorithm for state encoding

This paper presents an encoding technique that is common for many different logic synthesis problems. It enables us to construct a system of Boolean functions, and then to decompose this system into sub-systems in such a way that a dependency of functions, included into each sub-system, on the respective arguments is reduced. For complex applications such type of encoding has a high computational complexity and the paper proposes a novel evolutionary algorithm for the solution of this problemIFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

Recursive and iterative algorithms for N-ary search problems

The paper analyses and compares alternative iterative and recursive implementations of N-ary search algorithms in hardware (in field programmable gate arrays, in particular). The improvements over the previous results have been achieved with the aid of the proposed novel methods for the fast implementation of hierarchical algorithms. The methods possess the following distinctive features: 1) providing sub-algorithms with multiple entry points; 2) fast stack unwinding for exits from recursive sub-algorithms; 3) hierarchical returns based on two alternative approaches; 4) rational use of embedded memory blocks for the design of a hierarchical finite state machine.Applications in Artificial Intelligence - SearchRed de Universidades con Carreras en Informática (RedUNCI

Enriching traditional higher STEM education with online teaching and learning practices: students’ perspective

In this paper, we aim to identify online teaching and learning practices that would be beneficial for blended and traditional on-campus education within STEM (Science, Technology, Engineering, and Mathematics) courses. Our university, as well as the majority of higher education institutions worldwide, has had few to no experience in delivering full online courses before 2020. The teaching process was, however, severely affected and modified by the COVID-19 pandemic, forcing an abrupt and unprepared shift towards online education. In this work, we look at the pandemic as causing a very favorable side effect that forced the university to study, test, apply, and evaluate the benefits and drawbacks of online education and assessment methods. The study is a result of joint efforts from different departments at the University of Aveiro, Portugal, connected to STEM undergraduate and graduate programs and is based on a questionnaire targeted towards students. In total, 167 valid STEM students’ answers have been collected and analyzed, both quantitatively and qualitatively. As the result, the best teaching and learning practices are identified and the main difficulties and obstacles experienced by students are detected. Some of the problems are common to many higher education institutions, such as the lack of teacher preparation in delivering quality online synchronous and asynchronous classes, technical limitations (network bandwidth/weak equipment), ineffective communication during synchronous classes, gaps in student skills, and low activity of some students and even teachers. We believe that the presented results would allow for improving future on-campus, distance, and blended learning courses, particularly through avoiding less effective teaching and assessment methods and favoring those techniques that students consider more efficient. This ultimately would lead to a more rewarding teaching/learning experience.publishe