As the further development of single-core architectures faces seemingly insurmountable physical and technological limitations, computer designers have turned their attention to alternative approaches. One such promising alternative is the use of several smaller cores working in unison as a programmable hardware accelerator. It is clear that the vast – and, as yet, largely untapped – potential of hardware accelerators is coming to the forefront of computer architecture. There are many challenges that must be addressed for the programmable hardware accelerator to be realized in practice. In this thesis, load-balancing, on-chip communication, and an execution model are studied. Imbalanced distribution of workloads across the processing elements constitutes wasteful use of resources, which results in degrading the performance of the system. In this thesis, a hardware-based load-balancing technique is proposed, which is demonstrated to be more scalable than state-of-the-art loadbalancing techniques. To facilitate efficient communication among ever increasing number of cores, a scalable communication network is imperative. Packet switching networks-on-chip (NoC) is considered as a viable candidate for scalable communication fabric. The size of flit, which is a unit of flow control in NoC, is one of important design parameters that determine latency, throughput and cost of NoC routers. How to determine an optimal flit size is studied in this thesis and a novel router architecture is proposed, which overcomes a problem related with the flit size. This thesis also includes a new execution model and its supporting architecture. An event-driven model that is an extension of hardware description language is employed as an execution model. The dynamic scheduling and module-level prefetching for supporting the event-driven execution model are evaluated.

A demanda por vídeos de resolução ultra-alta (além de 1920x1080 pontos) levou à necessidade de desenvolvimento de padrões de codificação de vídeo novos e mais eficientes para prover alta eficiência de compressão. O novo padrão High Efficiency Video Coding (HEVC), publicado em 2013, atinge o dobro da eficiência de compressão (ou 50% de redução no tamanho do vídeo codificado) comparado com o padrão mais eficiente até então, e mais utilizado no mercado, o padrão H.264/AVC (Advanced Video Coding). O HEVC atinge este resultado ao custo de uma elevação da complexidade computacional das ferramentas inseridas no codificador e decodificador. O aumento do esforço computacional do padrão HEVC e as limitações de potência das tecnologias de fabricação em silício atuais tornam essencial o desenvolvimento de aceleradores de hardware para partes importantes da aplicação do HEVC. Aceleradores de hardware fornecem maior desempenho e eficiência energética para aplicações específicas que os processadores de propósito geral. Uma análise da aplicação do HEVC realizada neste trabalho identificou as partes mais importantes do HEVC do ponto de vista do esforço computacional, a saber, o Filtro de Interpolação de Ponto Fracionário, o Filtro de Deblocagem e o cálculo da Soma das Diferenças Absolutas. Uma análise de tempo de execução do Filtro de Interpolação indica um grande potencial de economia de potência/energia pela adaptação do acelerador de hardware à carga de trabalho variável. Esta tese introduz novas contribuições no tema de aceleradores dedicados e reconfiguráveis para o padrão HEVC. Aceleradores de hardware dedicados para o Filtro de Interpolação de Pixel Fracionário, para o Filtro de Deblocagem, e para o cálculo da Soma das Diferenças Absolutas, são propostos, projetados e avaliados nesta tese. A arquitetura de hardware proposta para o filtro de interpolação atinge taxa de processamento similar ao estado da arte, enquanto reduz a área do hardware para este bloco em 50%. A arquitetura de hardware proposta para o filtro de deblocagem também atinge taxa de processamento similar ao estado da arte com uma redução de 5X a 6X na contagem de gates e uma redução de 3X na dissipação de potência. A nova análise comparativa proposta para os elementos de processamento do cálculo da Soma das Diferenças Absolutas introduz diversas alternativas de projeto de arquitetura com diferentes resultados de área, desempenho e potência. A nova arquitetura reconfigurável para o filtro de interpolação do padrão HEVC fornece 57% de redução de área em tempo de projeto e adaptação da potência/energia em tempo-real a cada imagem processada, o que ainda não é suportado pelas arquiteturas do estado da arte para o filtro de interpolação. Adicionalmente, a tese propõe um novo esquema de alocação de aceleradores em tempo-real para arquiteturas reconfiguráveis baseadas em tiles de processamento e de grão-misto, o que reduz em 44% (23% em média) o “overhead” de comunicação comparado com uma estratégia first-fit com reuso de datapaths, para números diferentes de tiles e organizações internas de tile. Este esquema de alocação leva em conta a arquitetura interna para alocar aceleradores de uma maneira mais eficiente, evitando e minimizando a comunicação entre tiles. Os aceleradores e técnicas dedicadas e reconfiguráveis propostos nesta tese proporcionam implementações de codificadores de vídeo de nova geração, além do HEVC, com melhor área, desempenho e eficiência em potência. / The demand for ultra-high resolution video (beyond 1920x1080 pixels) led to the need of developing new and more efficient video coding standards to provide high compression efficiency. The High Efficiency Video Coding (HEVC) standard, published in 2013, reaches double compression efficiency (or 50% reduction in size of coded video) compared to the most efficient video coding standard at that time, and most used in the market, the H.264/AVC (Advanced Video Coding) standard. HEVC reaches this result at the cost of high computational effort of the tools included in the encoder and decoder. The increased computational effort of HEVC standard and the power limitations of current silicon fabrication technologies makes it essential to develop hardware accelerators for compute-intensive computational kernels of HEVC application. Hardware accelerators provide higher performance and energy efficiency than general purpose processors for specific applications. An HEVC application analysis conducted in this work identified the most compute-intensive kernels of HEVC, namely the Fractional-pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation. A run-time analysis on Interpolation Filter indicates a great potential of power/energy saving by adapting the hardware accelerator to the varying workload. This thesis introduces new contributions in the field of dedicated and reconfigurable hardware accelerators for HEVC standard. Dedicated hardware accelerators for the Fractional Pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation are herein proposed, designed and evaluated. The interpolation filter hardware architecture achieves throughput similar to the state of the art, while reducing hardware area by 50%. Our deblocking filter hardware architecture also achieves similar throughput compared to state of the art with a 5X to 6X reduction in gate count and 3X reduction in power dissipation. The thesis also does a new comparative analysis of Sum of Absolute Differences processing elements, in which various architecture design alternatives with different area, performance and power results were introduced. A novel reconfigurable interpolation filter hardware architecture for HEVC standard was developed, and it provides 57% design-time area reduction and run-time power/energy adaptation in a picture-by-picture basis, compared to the state-of-the-art. Additionally a run-time accelerator binding scheme is proposed for tile-based mixed-grained reconfigurable architectures, which reduces the communication overhead, compared to first-fit strategy with datapath reusing scheme, by up to 44% (23% on average) for different number of tiles and internal tile organizations. This run-time accelerator binding scheme is aware of the underlying architecture to bind datapaths in an efficient way, to avoid and minimize inter-tile communications. The new dedicated and reconfigurable hardware accelerators and techniques proposed in this thesis enable next-generation video coding standard implementations beyond HEVC with improved area, performance, and power efficiency.

A demanda por vídeos de resolução ultra-alta (além de 1920x1080 pontos) levou à necessidade de desenvolvimento de padrões de codificação de vídeo novos e mais eficientes para prover alta eficiência de compressão. O novo padrão High Efficiency Video Coding (HEVC), publicado em 2013, atinge o dobro da eficiência de compressão (ou 50% de redução no tamanho do vídeo codificado) comparado com o padrão mais eficiente até então, e mais utilizado no mercado, o padrão H.264/AVC (Advanced Video Coding). O HEVC atinge este resultado ao custo de uma elevação da complexidade computacional das ferramentas inseridas no codificador e decodificador. O aumento do esforço computacional do padrão HEVC e as limitações de potência das tecnologias de fabricação em silício atuais tornam essencial o desenvolvimento de aceleradores de hardware para partes importantes da aplicação do HEVC. Aceleradores de hardware fornecem maior desempenho e eficiência energética para aplicações específicas que os processadores de propósito geral. Uma análise da aplicação do HEVC realizada neste trabalho identificou as partes mais importantes do HEVC do ponto de vista do esforço computacional, a saber, o Filtro de Interpolação de Ponto Fracionário, o Filtro de Deblocagem e o cálculo da Soma das Diferenças Absolutas. Uma análise de tempo de execução do Filtro de Interpolação indica um grande potencial de economia de potência/energia pela adaptação do acelerador de hardware à carga de trabalho variável. Esta tese introduz novas contribuições no tema de aceleradores dedicados e reconfiguráveis para o padrão HEVC. Aceleradores de hardware dedicados para o Filtro de Interpolação de Pixel Fracionário, para o Filtro de Deblocagem, e para o cálculo da Soma das Diferenças Absolutas, são propostos, projetados e avaliados nesta tese. A arquitetura de hardware proposta para o filtro de interpolação atinge taxa de processamento similar ao estado da arte, enquanto reduz a área do hardware para este bloco em 50%. A arquitetura de hardware proposta para o filtro de deblocagem também atinge taxa de processamento similar ao estado da arte com uma redução de 5X a 6X na contagem de gates e uma redução de 3X na dissipação de potência. A nova análise comparativa proposta para os elementos de processamento do cálculo da Soma das Diferenças Absolutas introduz diversas alternativas de projeto de arquitetura com diferentes resultados de área, desempenho e potência. A nova arquitetura reconfigurável para o filtro de interpolação do padrão HEVC fornece 57% de redução de área em tempo de projeto e adaptação da potência/energia em tempo-real a cada imagem processada, o que ainda não é suportado pelas arquiteturas do estado da arte para o filtro de interpolação. Adicionalmente, a tese propõe um novo esquema de alocação de aceleradores em tempo-real para arquiteturas reconfiguráveis baseadas em tiles de processamento e de grão-misto, o que reduz em 44% (23% em média) o “overhead” de comunicação comparado com uma estratégia first-fit com reuso de datapaths, para números diferentes de tiles e organizações internas de tile. Este esquema de alocação leva em conta a arquitetura interna para alocar aceleradores de uma maneira mais eficiente, evitando e minimizando a comunicação entre tiles. Os aceleradores e técnicas dedicadas e reconfiguráveis propostos nesta tese proporcionam implementações de codificadores de vídeo de nova geração, além do HEVC, com melhor área, desempenho e eficiência em potência. / The demand for ultra-high resolution video (beyond 1920x1080 pixels) led to the need of developing new and more efficient video coding standards to provide high compression efficiency. The High Efficiency Video Coding (HEVC) standard, published in 2013, reaches double compression efficiency (or 50% reduction in size of coded video) compared to the most efficient video coding standard at that time, and most used in the market, the H.264/AVC (Advanced Video Coding) standard. HEVC reaches this result at the cost of high computational effort of the tools included in the encoder and decoder. The increased computational effort of HEVC standard and the power limitations of current silicon fabrication technologies makes it essential to develop hardware accelerators for compute-intensive computational kernels of HEVC application. Hardware accelerators provide higher performance and energy efficiency than general purpose processors for specific applications. An HEVC application analysis conducted in this work identified the most compute-intensive kernels of HEVC, namely the Fractional-pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation. A run-time analysis on Interpolation Filter indicates a great potential of power/energy saving by adapting the hardware accelerator to the varying workload. This thesis introduces new contributions in the field of dedicated and reconfigurable hardware accelerators for HEVC standard. Dedicated hardware accelerators for the Fractional Pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation are herein proposed, designed and evaluated. The interpolation filter hardware architecture achieves throughput similar to the state of the art, while reducing hardware area by 50%. Our deblocking filter hardware architecture also achieves similar throughput compared to state of the art with a 5X to 6X reduction in gate count and 3X reduction in power dissipation. The thesis also does a new comparative analysis of Sum of Absolute Differences processing elements, in which various architecture design alternatives with different area, performance and power results were introduced. A novel reconfigurable interpolation filter hardware architecture for HEVC standard was developed, and it provides 57% design-time area reduction and run-time power/energy adaptation in a picture-by-picture basis, compared to the state-of-the-art. Additionally a run-time accelerator binding scheme is proposed for tile-based mixed-grained reconfigurable architectures, which reduces the communication overhead, compared to first-fit strategy with datapath reusing scheme, by up to 44% (23% on average) for different number of tiles and internal tile organizations. This run-time accelerator binding scheme is aware of the underlying architecture to bind datapaths in an efficient way, to avoid and minimize inter-tile communications. The new dedicated and reconfigurable hardware accelerators and techniques proposed in this thesis enable next-generation video coding standard implementations beyond HEVC with improved area, performance, and power efficiency.

A demanda por vídeos de resolução ultra-alta (além de 1920x1080 pontos) levou à necessidade de desenvolvimento de padrões de codificação de vídeo novos e mais eficientes para prover alta eficiência de compressão. O novo padrão High Efficiency Video Coding (HEVC), publicado em 2013, atinge o dobro da eficiência de compressão (ou 50% de redução no tamanho do vídeo codificado) comparado com o padrão mais eficiente até então, e mais utilizado no mercado, o padrão H.264/AVC (Advanced Video Coding). O HEVC atinge este resultado ao custo de uma elevação da complexidade computacional das ferramentas inseridas no codificador e decodificador. O aumento do esforço computacional do padrão HEVC e as limitações de potência das tecnologias de fabricação em silício atuais tornam essencial o desenvolvimento de aceleradores de hardware para partes importantes da aplicação do HEVC. Aceleradores de hardware fornecem maior desempenho e eficiência energética para aplicações específicas que os processadores de propósito geral. Uma análise da aplicação do HEVC realizada neste trabalho identificou as partes mais importantes do HEVC do ponto de vista do esforço computacional, a saber, o Filtro de Interpolação de Ponto Fracionário, o Filtro de Deblocagem e o cálculo da Soma das Diferenças Absolutas. Uma análise de tempo de execução do Filtro de Interpolação indica um grande potencial de economia de potência/energia pela adaptação do acelerador de hardware à carga de trabalho variável. Esta tese introduz novas contribuições no tema de aceleradores dedicados e reconfiguráveis para o padrão HEVC. Aceleradores de hardware dedicados para o Filtro de Interpolação de Pixel Fracionário, para o Filtro de Deblocagem, e para o cálculo da Soma das Diferenças Absolutas, são propostos, projetados e avaliados nesta tese. A arquitetura de hardware proposta para o filtro de interpolação atinge taxa de processamento similar ao estado da arte, enquanto reduz a área do hardware para este bloco em 50%. A arquitetura de hardware proposta para o filtro de deblocagem também atinge taxa de processamento similar ao estado da arte com uma redução de 5X a 6X na contagem de gates e uma redução de 3X na dissipação de potência. A nova análise comparativa proposta para os elementos de processamento do cálculo da Soma das Diferenças Absolutas introduz diversas alternativas de projeto de arquitetura com diferentes resultados de área, desempenho e potência. A nova arquitetura reconfigurável para o filtro de interpolação do padrão HEVC fornece 57% de redução de área em tempo de projeto e adaptação da potência/energia em tempo-real a cada imagem processada, o que ainda não é suportado pelas arquiteturas do estado da arte para o filtro de interpolação. Adicionalmente, a tese propõe um novo esquema de alocação de aceleradores em tempo-real para arquiteturas reconfiguráveis baseadas em tiles de processamento e de grão-misto, o que reduz em 44% (23% em média) o “overhead” de comunicação comparado com uma estratégia first-fit com reuso de datapaths, para números diferentes de tiles e organizações internas de tile. Este esquema de alocação leva em conta a arquitetura interna para alocar aceleradores de uma maneira mais eficiente, evitando e minimizando a comunicação entre tiles. Os aceleradores e técnicas dedicadas e reconfiguráveis propostos nesta tese proporcionam implementações de codificadores de vídeo de nova geração, além do HEVC, com melhor área, desempenho e eficiência em potência. / The demand for ultra-high resolution video (beyond 1920x1080 pixels) led to the need of developing new and more efficient video coding standards to provide high compression efficiency. The High Efficiency Video Coding (HEVC) standard, published in 2013, reaches double compression efficiency (or 50% reduction in size of coded video) compared to the most efficient video coding standard at that time, and most used in the market, the H.264/AVC (Advanced Video Coding) standard. HEVC reaches this result at the cost of high computational effort of the tools included in the encoder and decoder. The increased computational effort of HEVC standard and the power limitations of current silicon fabrication technologies makes it essential to develop hardware accelerators for compute-intensive computational kernels of HEVC application. Hardware accelerators provide higher performance and energy efficiency than general purpose processors for specific applications. An HEVC application analysis conducted in this work identified the most compute-intensive kernels of HEVC, namely the Fractional-pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation. A run-time analysis on Interpolation Filter indicates a great potential of power/energy saving by adapting the hardware accelerator to the varying workload. This thesis introduces new contributions in the field of dedicated and reconfigurable hardware accelerators for HEVC standard. Dedicated hardware accelerators for the Fractional Pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation are herein proposed, designed and evaluated. The interpolation filter hardware architecture achieves throughput similar to the state of the art, while reducing hardware area by 50%. Our deblocking filter hardware architecture also achieves similar throughput compared to state of the art with a 5X to 6X reduction in gate count and 3X reduction in power dissipation. The thesis also does a new comparative analysis of Sum of Absolute Differences processing elements, in which various architecture design alternatives with different area, performance and power results were introduced. A novel reconfigurable interpolation filter hardware architecture for HEVC standard was developed, and it provides 57% design-time area reduction and run-time power/energy adaptation in a picture-by-picture basis, compared to the state-of-the-art. Additionally a run-time accelerator binding scheme is proposed for tile-based mixed-grained reconfigurable architectures, which reduces the communication overhead, compared to first-fit strategy with datapath reusing scheme, by up to 44% (23% on average) for different number of tiles and internal tile organizations. This run-time accelerator binding scheme is aware of the underlying architecture to bind datapaths in an efficient way, to avoid and minimize inter-tile communications. The new dedicated and reconfigurable hardware accelerators and techniques proposed in this thesis enable next-generation video coding standard implementations beyond HEVC with improved area, performance, and power efficiency.

In this thesis we present pvFPGA, the first system design solution for virtualizing an FPGA - based hardware accelerator on the x86 platform. The accelerator design on the FPGA can be used for accelerating various applications, regardless of the application computation latencies. Our design adopts the Xen virtual machine monitor (VMM) to build a paravirtualized environment, and a Xilinx Virtex - 6 as an FPGA accelerator. The accelerator communicates with the x86 server via PCI Express (PCIe). In comparison to the current GPU virtualization solutions, which primarily intercept and redirect API calls to the hosted or privileged domain’s user space, pvFPGA virtualizes an FPGA accelerator directly at the lower device driver layer. This gives rise to higher efficiency and lower overhead. In pvFPGA, each unprivileged domain allocates a shared data pool for both user - kernel and inter-domain data transfer. In addition, we propose the coprovisor, a new component that enables multiple domains to simultaneously access an FPGA accelerator. The experimental results have shown that 1) pvFPGA achieves close-to-zero overhead compared to accessing the FPGA accelerator without the VMM layer, 2) the FPGA accelerator is successfully shared by multiple domains, 3) distributing different maximum data transfer bandwidths to different domains can be achieved by regulating the size of the shared data pool at the split driver loading time, 4) request turnaround time is improved through DMA (Direct Memory Access) context switches implemented by the coprovisor.

abstract: QR decomposition (QRD) of a matrix is one of the most common linear algebra operationsused for the decomposition of a square/non-square matrix. It has a wide range of applications especially in Multiple Input-Multiple Output (MIMO) communication systems. Unfortunately it has high computation complexity { for matrix size of nxn, QRD has O(n3) complexity and back substitution, which is used to solve a system of linear equations, has O(n2) complexity. Thus, as the matrix size increases, the hardware resource requirement for QRD and back substitution increases signicantly. This thesis presents the design and implementation of a exible QRD and back substitution accelerator using a folded architecture. It can support matrix sizes of 4x4, 8x8, 12x12, 16x16, and 20x20 with low hardware resource requirement. The proposed architecture is based on the systolic array implementation of the Givens algorithm for QRD. It is built with three dierent types of computation blocks which are connected in a 2-D array structure. These blocks are controlled by a scheduler which facilitates reusability of the blocks to perform computation for any input matrix size which is a multiple of 4. These blocks are designed using two basic programming elements which support both the forward and backward paths to compute matrix R in QRD and column-matrix X in back substitution computation. The proposed architecture has been mapped to Xilinx Zynq Ultrascale+ FPGA (Field Programmable Gate Array), ZCU102. All inputs are complex with precision of 40 bits (38 fractional bits and 1 signed bit). The architecture can be clocked at 50 MHz. The synthesis results of the folded architecture for dierent matrix sizes are presented. The results show that the folded architecture can support QRD and back substitution for inputs of large sizes which otherwise cannot t on an FPGA when implemented using a at architecture. The memory sizes required for dierent matrix sizes are also presented. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

付記する学位プログラム名: 京都大学卓越大学院プログラム「先端光・電子デバイス創成学」 / 京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23325号 / 情博第761号 / 新制||情||130(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 小野寺 秀俊, 教授 大木 英司, 教授 佐藤 高史 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Deep Neural Networks are state-of the art algorithms for various image and natural language processing tasks. These networks are composed of billions of operations working on an input to produce the desired result. Along with this computational complexity, these workloads are also massively parallel in nature. These inherent properties make deep neural networks an excellent target for custom acceleration. The main challenge faced by such accelerators is achieving a compromise between power consumption, software programmability, and resource utilization for the varied compute and data access patterns presented by DNN workloads. In this work, I present Inference Engine, a scalable and efficient DNN accelerator designed to be agnostic to the type of DNN workload. Inference Engine was designed to provide near peak hardware resource utilization, minimize data transfer, and offer a programmer friendly instruction set. Inference engine scales at the level of individually programmable clusters, each of which contains several hundred compute resources. It provides an instruction set designed to exploit parallelism within the workload while also allowing freedom for compiler based exploration of data access patterns.

abstract: While machine/deep learning algorithms have been successfully used in many practical applications including object detection and image/video classification, accurate, fast, and low-power hardware implementations of such algorithms are still a challenging task, especially for mobile systems such as Internet of Things, autonomous vehicles, and smart drones. This work presents an energy-efficient programmable application-specific integrated circuit (ASIC) accelerator for object detection. The proposed ASIC supports multi-class (face/traffic sign/car license plate/pedestrian), many-object (up to 50) in one image with different sizes (6 down-/11 up-scaling), and high accuracy (87% for face detection datasets). The proposed accelerator is composed of an integral channel detector with 2,000 classifiers for five rigid boosted templates to make a strong object detection. By jointly optimizing the algorithm and efficient hardware architecture, the prototype chip implemented in 65nm demonstrates real-time object detection of 20-50 frames/s with 22.5-181.7mW (0.54-1.75nJ/pixel) at 0.58-1.1V supply. In this work, to reduce computation without accuracy degradation, an energy-efficient deep convolutional neural network (DCNN) accelerator is proposed based on a novel conditional computing scheme and integrates convolution with subsequent max-pooling operations. This way, the total number of bit-wise convolutions could be reduced by ~2x, without affecting the output feature values. This work also has been developing an optimized dataflow that exploits sparsity, maximizes data re-use and minimizes off-chip memory access, which can improve upon existing hardware works. The total off-chip memory access can be saved by 2.12x. Preliminary results of the proposed DCNN accelerator achieved a peak 7.35 TOPS/W for VGG-16 by post-layout simulation results in 40nm. A number of recent efforts have attempted to design custom inference engine based on various approaches, including the systolic architecture, near memory processing, and in-meomry computing concept. This work evaluates a comprehensive comparison of these various approaches in a unified framework. This work also presents the proposed energy-efficient in-memory computing accelerator for deep neural networks (DNNs) by integrating many instances of in-memory computing macros with an ensemble of peripheral digital circuits, which supports configurable multibit activations and large-scale DNNs seamlessly while substantially improving the chip-level energy-efficiency. Proposed accelerator is fully designed in 65nm, demonstrating ultralow energy consumption for DNNs. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

La détection, localisation d'obstacles et la reconstruction de carte d'occupation 2D sont des fonctions de base pour un robot navigant dans un environnement intérieure lorsque l'intervention avec les objets se fait dans un environnement encombré. Les solutions fondées sur la vision artificielle et couramment utilisées comme SLAM (simultaneous localization and mapping) ou le flux optique ont tendance a être des calculs intensifs. Ces solutions nécessitent des ressources de calcul puissantes pour répondre à faible vitesse en temps réel aux contraintes. Nous présentons une architecture matérielle pour la détection, localisation d'obstacles et la reconstruction de cartes d'occupation 2D en temps réel. Le système proposé est réalisé en utilisant une architecture de vision sur FPGA (field programmable gates array) et des capteurs d'odométrie pour la détection, localisation des obstacles et la cartographie. De la fusion de ces deux sources d'information complémentaires résulte un modèle amelioré de l'environnement autour des robots. L'architecture proposé est un système à faible coût avec un temps de calcul réduit, un débit d'images élevé, et une faible consommation d'énergie / Obstacle detection, localization and occupancy map reconstruction are essential abilities for a mobile robot to navigate in an environment. Solutions based on passive monocular vision such as simultaneous localization and mapping (SLAM) or optical flow (OF) require intensive computation. Systems based on these methods often rely on over-sized computation resources to meet real-time constraints. Inverse perspective mapping allows for obstacles detection at a low computational cost under the hypothesis of a flat ground observed during motion. It is thus possible to build an occupancy grid map by integrating obstacle detection over the course of the sensor. In this work we propose hardware/software system for obstacle detection, localization and 2D occupancy map reconstruction in real-time. The proposed system uses a FPGA-based design for vision and proprioceptive sensors for localization. Fusing this information allows for the construction of a simple environment model of the sensor surrounding. The resulting architecture is a low-cost, low-latency, high-throughput and low-power system.