The number and the size of sensor networks, e.g., used for monitoring of public places, are steadily increasing, introducing new demands on the algorithms used to process the collected measurements. The straightforward solution is centralised fusion, where all measurements are sent to a common node where all estimation is performed. This can be shown to be optimal, but it is resource intensive, scales poorly, and is sensitive to communication and sensor node failure. The alternative is to perform decentralised fusion, where the computations are spread out in the network. Distributing the computation results in an algorithm that scales better with the size of the network and that can be more robust to hardware failure. The price of decentralisation is that it is more difficult to provide optimal estimates. Hence, a decentralised method needs to be designed to maximise scaling and robustness while minimising the performance loss. This MSc thesis studies tree aspects of the design of decentralised networks: the network topology, communication schemes, and methods to fuse the estimates from different sensor nodes. Results are obtained using simulations of a network consisting of radar sensors, where the quality of the estimates are compared(the root mean square error, RMSE) and the consistency of the estimates (the normalised estimation error squared, NEES). Based on the simulation, it is recommended that a 2-tree network topology should be used, and that estimates should be communicated throughout the network using an algorithm that allows information to propagate. This is achieved by sending information in two steps. The first step is to let the nodes send information to their neighbours with a certain frequency, after which a fusion is performed. The second step is to let the nodes indirectly forward the information they receive by sending the result of the fusion. This second step is not performed every time information is received, but rather at an interval, e.g., every fifth time. Furthermore, 3 sub-optimal methods to fuse possibly correlated estimates are evaluated: Covariance Intersection, Safe Fusion, and Inverse Covariance Intersection. The outcome is to recommend using Inverse Covariance Intersection.

Processadores embarcados utilizam eficientemente o paralelismo a nível de instrução para atender as necessidades de desempenho e energia em aplicações atuais. Embora a melhoria de performance seja um dos principais objetivos em processadores em geral, ela pode levar a um impacto negativo no consumo de energia, uma restrição crítica para sistemas atuais. Nesta dissertação, apresentamos o SoMMA, uma arquitetura de memória gerenciada por software para processadores embarcados capaz de reduz consumo de energia e energy-delay product (EDP), enquanto ainda aumenta a banda de memória. A solução combina o uso de memórias gerenciadas por software com a cache de dados, de modo a reduzir o consumo de energia e EDP do sistema. SoMMA também melhora a performance do sistema, pois os acessos à memória podem ser realizados em paralelo, sem custo em portas de memória extra na cache de dados. Transformações de código do compilador auxiliam o programador a utilizar a arquitetura proposta. Resultados experimentais mostram que SoMMA é mais eficiente em termos de energia e desempenho tanto a nível de processador quanto a nível do sistema completo. A técnica apresenta speedups de 1.118x e 1.121x, consumindo 11% e 12.8% menos energia quando comparando processadores que utilizam e não utilizam SoMMA. Há ainda redução de até 41.5% em EDP do sistema, sempre mantendo a área dos processadores equivalentes. Por fim, SoMMA também reduz o número de cache misses quando comparado ao processador baseline. / Embedded processors rely on the efficient use of instruction-level parallelism to answer the performance and energy needs of modern applications. Though improving performance is the primary goal for processors in general, it might lead to a negative impact on energy consumption, a particularly critical constraint for current systems. In this dissertation, we present SoMMA, a software-managed memory architecture for embedded multi-issue processors that can reduce energy consumption and energy-delay product (EDP), while still providing an increase in memory bandwidth. We combine the use of software-managed memories (SMM) with the data cache, and leverage the lower energy access cost of SMMs to provide a processor with reduced energy consumption and EDP. SoMMA also provides a better overall performance, as memory accesses can be performed in parallel, with no cost in extra memory ports. Compiler-automated code transformations minimize the programmer’s effort to benefit from the proposed architecture. Our experimental results show that SoMMA is more energy- and performance-efficient not only for the processing cores, but also at full-system level. Comparisons were done using the VEX processor, a VLIW reconfigurable processor. The approach shows average speedups of 1.118x and 1.121x, while consuming up to 11% and 12.8% less energy when comparing two modified processors and their baselines. SoMMA also shows reduction of up to 41.5% on full-system EDP, maintaining the same processor area as baseline processors. Lastly, even with SoMMA halving the data cache size, we still reduce the number of data cache misses in comparison to baselines.

Processadores embarcados utilizam eficientemente o paralelismo a nível de instrução para atender as necessidades de desempenho e energia em aplicações atuais. Embora a melhoria de performance seja um dos principais objetivos em processadores em geral, ela pode levar a um impacto negativo no consumo de energia, uma restrição crítica para sistemas atuais. Nesta dissertação, apresentamos o SoMMA, uma arquitetura de memória gerenciada por software para processadores embarcados capaz de reduz consumo de energia e energy-delay product (EDP), enquanto ainda aumenta a banda de memória. A solução combina o uso de memórias gerenciadas por software com a cache de dados, de modo a reduzir o consumo de energia e EDP do sistema. SoMMA também melhora a performance do sistema, pois os acessos à memória podem ser realizados em paralelo, sem custo em portas de memória extra na cache de dados. Transformações de código do compilador auxiliam o programador a utilizar a arquitetura proposta. Resultados experimentais mostram que SoMMA é mais eficiente em termos de energia e desempenho tanto a nível de processador quanto a nível do sistema completo. A técnica apresenta speedups de 1.118x e 1.121x, consumindo 11% e 12.8% menos energia quando comparando processadores que utilizam e não utilizam SoMMA. Há ainda redução de até 41.5% em EDP do sistema, sempre mantendo a área dos processadores equivalentes. Por fim, SoMMA também reduz o número de cache misses quando comparado ao processador baseline. / Embedded processors rely on the efficient use of instruction-level parallelism to answer the performance and energy needs of modern applications. Though improving performance is the primary goal for processors in general, it might lead to a negative impact on energy consumption, a particularly critical constraint for current systems. In this dissertation, we present SoMMA, a software-managed memory architecture for embedded multi-issue processors that can reduce energy consumption and energy-delay product (EDP), while still providing an increase in memory bandwidth. We combine the use of software-managed memories (SMM) with the data cache, and leverage the lower energy access cost of SMMs to provide a processor with reduced energy consumption and EDP. SoMMA also provides a better overall performance, as memory accesses can be performed in parallel, with no cost in extra memory ports. Compiler-automated code transformations minimize the programmer’s effort to benefit from the proposed architecture. Our experimental results show that SoMMA is more energy- and performance-efficient not only for the processing cores, but also at full-system level. Comparisons were done using the VEX processor, a VLIW reconfigurable processor. The approach shows average speedups of 1.118x and 1.121x, while consuming up to 11% and 12.8% less energy when comparing two modified processors and their baselines. SoMMA also shows reduction of up to 41.5% on full-system EDP, maintaining the same processor area as baseline processors. Lastly, even with SoMMA halving the data cache size, we still reduce the number of data cache misses in comparison to baselines.

Processadores embarcados utilizam eficientemente o paralelismo a nível de instrução para atender as necessidades de desempenho e energia em aplicações atuais. Embora a melhoria de performance seja um dos principais objetivos em processadores em geral, ela pode levar a um impacto negativo no consumo de energia, uma restrição crítica para sistemas atuais. Nesta dissertação, apresentamos o SoMMA, uma arquitetura de memória gerenciada por software para processadores embarcados capaz de reduz consumo de energia e energy-delay product (EDP), enquanto ainda aumenta a banda de memória. A solução combina o uso de memórias gerenciadas por software com a cache de dados, de modo a reduzir o consumo de energia e EDP do sistema. SoMMA também melhora a performance do sistema, pois os acessos à memória podem ser realizados em paralelo, sem custo em portas de memória extra na cache de dados. Transformações de código do compilador auxiliam o programador a utilizar a arquitetura proposta. Resultados experimentais mostram que SoMMA é mais eficiente em termos de energia e desempenho tanto a nível de processador quanto a nível do sistema completo. A técnica apresenta speedups de 1.118x e 1.121x, consumindo 11% e 12.8% menos energia quando comparando processadores que utilizam e não utilizam SoMMA. Há ainda redução de até 41.5% em EDP do sistema, sempre mantendo a área dos processadores equivalentes. Por fim, SoMMA também reduz o número de cache misses quando comparado ao processador baseline. / Embedded processors rely on the efficient use of instruction-level parallelism to answer the performance and energy needs of modern applications. Though improving performance is the primary goal for processors in general, it might lead to a negative impact on energy consumption, a particularly critical constraint for current systems. In this dissertation, we present SoMMA, a software-managed memory architecture for embedded multi-issue processors that can reduce energy consumption and energy-delay product (EDP), while still providing an increase in memory bandwidth. We combine the use of software-managed memories (SMM) with the data cache, and leverage the lower energy access cost of SMMs to provide a processor with reduced energy consumption and EDP. SoMMA also provides a better overall performance, as memory accesses can be performed in parallel, with no cost in extra memory ports. Compiler-automated code transformations minimize the programmer’s effort to benefit from the proposed architecture. Our experimental results show that SoMMA is more energy- and performance-efficient not only for the processing cores, but also at full-system level. Comparisons were done using the VEX processor, a VLIW reconfigurable processor. The approach shows average speedups of 1.118x and 1.121x, while consuming up to 11% and 12.8% less energy when comparing two modified processors and their baselines. SoMMA also shows reduction of up to 41.5% on full-system EDP, maintaining the same processor area as baseline processors. Lastly, even with SoMMA halving the data cache size, we still reduce the number of data cache misses in comparison to baselines.