Uma arquitetura para execução de codigo comprimido em sistemas dedicados

Orientador : Guido Costa Souza de AraujoTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Projetos de sistemas dedicados modernos têm exigido cada vez mais memória de programa para incluir novas funcionalidades como interface com o usuário, suporte a novos componentes, etc. O aumento no tamanho dos programas tem feito com que a área ocupada pela memória em um circuito integrado moderno seja um dos fatores determinantes no seu custo final bem como um dos maiores responsáveis pelo consumo de potência nestes dispositivos. A compressão de código de programa vem sendo considerada como uma estratégia importante na minimização deste problema. Esta tese trata da compressão de programas para execução em sistemas dedicados baseados em arquiteturas RISC. Um amplo estudo demonstra que a utilização do método proposto neste trabalho, Instruction Based Compression (IBC), resulta em boas razões de compressão e implementações eficientes de descompressores. Para a arquitetura MIPS foi obtida a melhor razão de compressão (tamanho final do programa comprimido e do descompressor em relação ao programa original) conhecida (53,6%) utilizando como benchmark programas do SPEC CINT'95. Uma arquitetura pipelined para o descompressor é proposta e um protótipo foi implementado para o processador Leon (SPARC V8). Esta é a primeira implementação em hardware de um descompressor para a arquitetura SPARC, tendo produzido uma razão de compressão de 61,8% para o mesmo benchmark e uma queda de apenas 5,89% no desempenho médio do sistemaAbstract: The demand for program memory in embedded systems has grown considerably in recent years, as a result of the need to accommodate new system functionalities such as novel user interfaces, additional hardware devices, etc. The increase in program size has turned memory into the largest single factor in the total area and power dissipation of a modern System-on-a-Chíp (SoC). Program code compression has been considered recently a central technique in reducing the cost of memory in such systems. This thesis studies the code compression problem for RISC architectures. A thorough experimental study shows that the Instructíon Based Compressíon (IBC) technique proposed herein results in very good compression ratios and efficient decompressor engine implementations. For the MIPS architecture this approach results in the best compression ratio (size of the compressed program divided by the size of the original program) known in the literature (53.6%), when it is evaluated using the SPEC CINT'95 benchmark programs. A decompressor pipelined architecture was developed and prototyped for the Leon (SPARC V8) processor. This is the first implementation of a hardware decompressor on the SPARC architecture, having resulted in a 61.8% compression ratio for the same benchmark, at the expense of a fairly small performance overhead (5.89% on average)DoutoradoDoutor em Ciência da Computaçã

ReonV: uma versão RISC-V do processador SPARC LEON3

Este artigo reforça a importância de reutilização e contribuição para o desenvolvimento de hardware de código aberto, mostrando o exemplo de desenvolvimento de um processador soft-core RISC-V, dado o nome de ReonV, que foi desenvolvido reutilizando todos os módulos de um processador SPARC V8 de 32 bits já consolidado, modificando apenas seu pipeline para a nova ISA e herdando todos os outros módulos e o Board Support Package (BSP) do processador original

O Fórum e a Aprendizagem Ativa na EAD

This study aims to present reflections on the forum as a tool for more active learning in Distance Education, proposing educational practices and posting forms - based on contributions from neuroscience and research results - with the intention of stimulating interactivity in the forums and a deeper and more meaningful appropriation of knowledge. The generating problem of the study was the low participation of students in the forums of distance graduation disciplines. However, with the research, it was observed that the frequency of access to forums is high, but active participation is low, which highlights the need to be constantly encouraged with posts that generate effective interaction; with close, adequate and constant mediation; and with good practice guidelines for acting in forums for teachers, mediators and students. We noticed that students seek out forums to clarify doubts, but also to show their opinion, seek acceptance and have a sense of belonging to the community, factors that have a strong influence on the learning process. The qualitative-quantitative research used the Design Thinking methodology and included SWOT analysis and systematic observation of forums, focus groups with students, interviews with university leaders and mediators, the authors' experience as learning mediators and a questionnaire answered by 1865 distance undergraduate students. Keywords: Active learning. Neuroscience. Distance learning. Interaction. Forum. Este estudo visa apresentar reflexões sobre o fórum como ferramenta para uma aprendizagem mais ativa na Educação a Distância (EAD), propondo práticas educativas e formas de postagem – baseadas em aportes da neurociência e nos resultados de pesquisa – com a intenção de estimular a interatividade nos fóruns e a apropriação mais profunda e significativa do conhecimento. O problema gerador do estudo foi a baixa participação dos alunos nos fóruns de disciplinas de graduação à distância. No entanto, com a pesquisa, observou-se que a frequência de acesso aos fóruns é alta, porém a participação ativa é baixa, o que evidencia a necessidade de ser constantemente estimulada com posts que gerem interação efetiva; com mediação próxima, adequada e constante; e com orientações de boas práticas para atuação nos fóruns voltadas a professores, mediadores e alunos. Percebemos que os alunos buscam os fóruns para esclarecer dúvidas e também para mostrar sua opinião, buscar acolhimento e ter senso de pertencimento à comunidade, fatores de forte influência no processo de aprendizagem. A pesquisa quali-quantitativa utilizou a metodologia do Design Thinking e contou com análise de SWOT e observação sistemática dos fóruns, grupos focais com alunos, entrevistas com dirigentes e facilitadores de uma universidade, a experiência das autoras como facilitadoras de aprendizagem e questionário respondido por 1865 alunos de graduação a distância.   Palavras-chave: Aprendizagem ativa. Neurociência. EAD. Interação. Fórum

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

ReonV: uma versão RISC-V do processador SPARC LEON3

Este artigo reforça a importância de reutilização e contribuição para o desenvolvimento de hardware de código aberto, mostrando o exemplo de desenvolvimento de um processador soft-core RISC-V, dado o nome de ReonV, que foi desenvolvido reutilizando todos os módulos de um processador SPARC V8 de 32 bits já consolidado, modificando apenas seu pipeline para a nova ISA e herdando todos os outros módulos e o Board Support Package (BSP) do processador original

A New Technique for Instruction Encoding in High Performance Architectures

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