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Sökning: WFRF:(Schiavoni V.)

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  • Kaiser, M., et al. (författare)
  • VEDLIoT: Very Efficient Deep Learning in IoT
  • 2022
  • Ingår i: Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022. - : IEEE. - 9783981926361
  • Konferensbidrag (refereegranskat)abstract
    • The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
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  • Ménétrey, J., et al. (författare)
  • Attestation Mechanisms for Trusted Execution Environments Demystified
  • 2022
  • Ingår i: <em>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</em>. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031160912 ; , s. 95-113
  • Konferensbidrag (refereegranskat)abstract
    • Attestation is a fundamental building block to establish trust over software systems. When used in conjunction with trusted execution environments, it guarantees the genuineness of the code executed against powerful attackers and threats, paving the way for adoption in several sensitive application domains. This paper reviews remote attestation principles and explains how the modern and industrially well-established trusted execution environments Intel SGX, Arm TrustZone and AMD SEV, as well as emerging RISC-V solutions, leverage these mechanisms. 
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4.
  • Salami, B., et al. (författare)
  • LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing
  • 2020
  • Ingår i: PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020). - 1530-1591. - 9783981926347 ; , s. 169-174
  • Konferensbidrag (refereegranskat)abstract
    • The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.
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  • Resultat 1-5 av 5

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