BEGIN:VCALENDAR VERSION:2.0 PRODID:-//DATE2026//date-conference.com//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20260422T083000 DTEND;TZID=Europe/Rome:20260422T123000 LOCATION:Room Figaro CREATED:20260421T174602 DTSTAMP:20260421T174602 SUMMARY:W03 Reconciling Implementation Performance and Confidence in Machine Learning URL;VALUE=URI:https://date26date-conference.com/programme#W03 BEGIN:VALARM TRIGGER:-PT15M ACTION:DISPLAY DESCRIPTION:Reminder END:VALARM DESCRIPTION:Get the latest session information at https://date26date-conference.com/programme#W03\n\n\nThis workshop addresses the challenge of reconciling performance and confidence in the implementation of machine learning algorithms for safety-critical domains. While optimized hardware platforms (GPU, FPGA, accelerators) and advanced graph transformations enable high performance, they raise concerns regarding traceability, verification, and certification. The focus will be on bridging the gap between training and implementation models, specifying design models, handling numerical accuracy, and mastering optimization techniques. Presentations will highlight industrial and academic perspectives, with emphasis on certification constraints (e.g., DO-178) and assurance arguments. The format combines a keynote with a series of short technical talks (15 min) to stimulate discussions (15 min).\n \n The ultimate goal is to foster a common understanding of how to implement ML efficiently while ensuring reliability and safety. X-ALT-DESC;FMTTYPE=text/html:

Get the latest session information at https://date26date-conference.com/programme#W03

This workshop addresses the challenge of reconciling performance and confidence in the implementation of machine learning algorithms for safety-critical domains. While optimized hardware platforms (GPU, FPGA, accelerators) and advanced graph transformations enable high performance, they raise concerns regarding traceability, verification, and certification. The focus will be on bridging the gap between training and implementation models, specifying design models, handling numerical accuracy, and mastering optimization techniques. Presentations will highlight industrial and academic perspectives, with emphasis on certification constraints (e.g., DO-178) and assurance arguments. The format combines a keynote with a series of short technical talks (15 min) to stimulate discussions (15 min).

The ultimate goal is to foster a common understanding of how to implement ML efficiently while ensuring reliability and safety.

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