ACHIEVE · AdvanCed Hardware/Software Components for Integrated/Embedded Vision SystEms

AdvanCed Hardware/Software Components for Integrated/Embedded Vision SystEms

Applications like assisted and autonomous driving, unsupervised surveillance or robot vision, mandate real-time interpretation of the scene. This requires the incorporation of a higher level of intelligence at sensor level in order to extract the relevant information. The goal is to analyse the visual stimulus and to elaborate an adequate representation of the scene right at the sensor plane. This has very positive consequences for the power efficiency of the system. Current trends in object recognition and classification rely on representation learning. Provided with a flexible internal structure and enough computing power, modern machine-learning systems has superseded the extraction of handcrafted features in favour of automatically discovering the most appropriate internal representation. This deep learning approach has revolutionized object recognition by dramatically reducing the error rate. The challenge today is to convey these processing capabilities to compact, lightweight and power-aware embedded vision systems and vision systems-on-a-chip. Moreover, in application scenarios like autonomous surveillance or intelligent transportation systems, these embedded vision systems will also be networked. A centralized processing is unpractical, it is necessary to develop a distributed system in which smart devices cooperate towards a collective goal. The approach to take these challenges needs to be multidisciplinary. Efficient analysis and multilevel optimization techniques are required. ACHIEVE-ITN has trained a new generation of scientists through a research programme on highly integrated HW-SW components for the implementation of ultra-efficient embedded vision systems as the basis for innovative distributed vision applications. The mjor achievements of our research program are: - The development of integrated sensing and processing chips that combine image capture with on-chip acceleration of feature extraction/learning with a limited number of resources and under a restricted power budget - The conception of a chip architecture that extracts 2D and 3D information at sensor level for an enriched description of the scene that can be shared and combined between groups of camera nodes working in cooperation - The design of hardware accelerators that will permit the implementation of heavy-duty feature and representation learning and deep learning inference - The conception of compact and efficient reconfigurable embedded vision systems, where local processing of visual information is combined with agile transmission of metadata and a careful power management - The development of the cooperative vision algorithms that will operate on an enriched representation of the scene that can be locally shared by a set of nodes, allowing them to react collectively - Discarding the concept of a central hub where all the data crunching is performed in favour of a scalable distributed processing system in which visual information drives dynamic adaptation and feedback to enhance users’ experience - The introduction of scalable, easily deployable, always-on, visual monitoring methods that will be the basis for a new class of products and services

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