Modern vehicles in many segments of the vehicular domain need to communicate and collaborate to achieve a joint functionality in, e.g., an autonomous quarry, mine or a recycling site. To provide such functionality, these vehicles need to be equipped with high data-rate sensors (e.g., cameras and lidars). The large amount of data acquired from these sensors needs to be communicated within as well as among the vehicles with predictable low latencies. The traditional intra-vehicle communication (based on field buses) and inter-vehicle communication (based on WiFi and 4G) are becoming a bottleneck in meeting the high-bandwidth and low-latency communication requirements. The recently introduced IEEE Time-Sensitive Networking (TSN) standards and 5G communication offer promising solutions to address these requirements within and among the vehicles respectively. Alas, there is a lack of a holistic software development framework and execution environment for predictable vehicular systems that utilise blended TSN-5G communication. This lack hinders the vehicle industry from taking full advantage of these ground- breaking technologies. The aim of PROVIDENT is to develop novel techniques to provide a full- fledged holistic software development environment for vehicular systems that utilise blended TSN- 5G communication. The benefits for the vehicle industry include cost-efficient system development, better quality of developed functions to lower costs, and better use of expensive and scarce computing and communication resources. A major trait of the project consortium is that it offers a clear value chain initiating from academia (MDH); through a tools developer (Arcticus Systems); and finally to an end user of the technology (HIAB).
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Computation reuse-aware accelerator for neural networks (May 2020) Hoda Mahdiani , Alireza Khadem , Ali Yasoubi , Azam Ghanbari , Mehdi Modarressi, Masoud Daneshtalab Institution of Engineering and Technology (IET)
Hardware Acceleration for Recurrent Neural Networks (May 2020) Sima Sinaei, Masoud Daneshtalab Institution of Engineering and Technology (IET)
Feedforward Neural Networks on Massively Parallel Architectures (May 2020) Reza Hojabr , Ahmad Khonsari , Mehdi Modarressi, Masoud Daneshtalab Institution of Engineering and Technology (IET)