We present CHERI Concentrate, a new fat-pointer compression scheme applied to CHERI, the most developed capability-pointer system at present. Capability fat-pointers are a primary candidate for enforcing fine-grained and non-bypassable security properties in future computer systems, although increased pointer size can severely affect performance. Thus, several proposals for capability compression have been suggested but these did not support legacy instruction sets, ignored features critical to the existing software base, and also introduced design inefficiencies to RISC-style processor pipelines. CHERI Concentrate improves on the state-of-the-art region-encoding efficiency, solves important pipeline problems, and eases semantic restrictions of compressed encoding, allowing it to protect a full legacy software stack. We analyze and extend logic from the open-source CHERI prototype processor design on FPGA to demonstrate encoding efficiency, minimize delay of pointer arithmetic, and eliminate additional load-to-use delay. To verify correctness of our proposed high-performance logic, we present a HOL4 machine-checked proof of the decode and pointer-modify operations. Finally, we measure a 50%-75% reduction in L2 misses for many compiled C-language benchmarks running under a commodity operating system using compressed 128-bit and 64-bit formats, demonstrating both compatibility with and increased performance over the uncompressed, 256-bit format.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present CHERI Concentrate, a new fat-pointer compression scheme applied to CHERI, the most developed capability-pointer system at present. Capability fat-pointers are a primary candidate for enforcing fine-grained and non-bypassable security properties in future computer systems, although increased pointer size can severely affect performance. Thus, several proposals for capability compression have been suggested but these […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13552,13558],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-593287","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-hardware-devices","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-4-30","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Transactions on Computers","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"doi","viewUrl":"false","id":"false","title":"10.1109\/TC.2019.2914037","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.cl.cam.ac.uk\/research\/security\/ctsrd\/pdfs\/2019tc-cheri-concentrate.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Jonathan Woodruff","user_id":0,"rest_url":false},{"type":"text","value":"Alexandre Joannou","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Hongyan Xia","user_id":41260,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Hongyan Xia"},{"type":"text","value":"Anthony Fox","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Robert Norton-Wright","user_id":39045,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Robert Norton-Wright"},{"type":"text","value":"Thomas Bauereiss","user_id":0,"rest_url":false},{"type":"user_nicename","value":"David Chisnall","user_id":38034,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=David Chisnall"},{"type":"text","value":"Brooks Davis","user_id":0,"rest_url":false},{"type":"text","value":"Khilan Gudka","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Nathaniel Filardo","user_id":39390,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nathaniel Filardo"},{"type":"text","value":"A. 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