Table of contents

Description

  • Speaker

    Robert Watson - University of Cambridge

CHERI is a processor architecture protection model enabling fine-grained C/C++ memory protection and scalable software compartmentalization. CHERI hybridizes conventional processor, instruction-set, and software designs with an architectural capability model. Originating in DARPA’s CRASH research program in 2010, the work has progressed from FPGA prototypes to the recently released Arm Morello prototype processor and SoC implementing CHERI principles, Microsoft’s CHERIoT microcontroller, and multiple commercial products shipping from 2025 onwards. This talk will introduce the design principles of CHERI, explain how software works on the platform, and explore the large-scale evaluation case studies based on tens of millions of lines of open-source code. It will conclude by exploring future research directions as well as in-progress transition into industrial use.

Practical infos

Next sessions

  • CHERI standardization and software ecosystem

    • September 12, 2025 (11:00 - 12:00)

    • Inria Centre of the University of Rennes - Room Métivier

    Speaker : Carl Shaw - Codasip

    This talk will describe the current status of the RISC-V International standardization process to add CHERI as an official extension to RISC-V. It will then explore the current state of CHERI-enabled operating systems, toolchains and software tool development, focusing on the CHERI-RISC-V hardware implementations of CHERI. It will then go on to give likely future development roadmaps and how the[…]

    • SoSysec

    • SemSecuElec

    • Compartmentalization

    • Operating system and virtualization

    • Hardware/software co-design

    • Hardware architecture

  • Towards privacy-preserving and fairness-aware federated learning framework

    • September 19, 2025 (11:00 - 12:00)

    • Inria Center of the University of Rennes - - Room TBD

    Speaker : Nesrine Kaaniche - Télécom SudParis

    Federated Learning (FL) enables the distributed training of a model across multiple data owners under the orchestration of a central server responsible for aggregating the models generated by the different clients. However, the original approach of FL has significant shortcomings related to privacy and fairness requirements. Specifically, the observation of the model updates may lead to privacy[…]

    • Cryptography

    • SoSysec

    • Privacy

    • Machine learning

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