Presentation
SIEMENS: Intelligence Between the Runs: Agentic AI for Feedback-Driven Verification
DescriptionAs AI/ML SoCs, chiplet-based architectures, and software-defined systems increase design complexity, verification is no longer defined only by design size. Increasingly, complexity comes from interaction, iteration, and execution context: how IP blocks, protocols, power states, software, and workloads behave when composed into a system. As a result, verification has become a feedback-driven process in which each run can reshape intent, change priorities, expose new assumptions, and determine what must happen next.
Much of today’s verification effort now lives both within the tools and between the runs: planning, static analysis, triage, interpretation, debug, coverage analysis, and re-steering. AI-enhanced verification engines can accelerate specific tasks, while agentic AI can help connect those tasks into context-aware workflows that are easier to guide, analyze, and close. Together, these approaches bring intelligence not only to individual verification engines, but also to the engineering decisions that connect them.
Rather than treating AI as a chatbot or isolated code generator, agentic verification connects design intent, verification plans, RTL, testbenches, assertions, coverage, logs, waveforms, and engine results so AI can help engineers plan, execute, analyze, and refine verification tasks under human-defined governance. The session will examine the foundations required for trusted AI-driven flows, including domain-scoped agents, tool-aware context, persistent verification knowledge, traceability from recommendation to evidence, and orchestration across simulation, formal, emulation, and hardware-assisted validation.
Attendees will leave with a framework for identifying where AI can deliver near-term productivity gains inside verification tools, where agentic workflows can reduce friction across iterations, and how engineering judgment remains central to trusted signoff.
Much of today’s verification effort now lives both within the tools and between the runs: planning, static analysis, triage, interpretation, debug, coverage analysis, and re-steering. AI-enhanced verification engines can accelerate specific tasks, while agentic AI can help connect those tasks into context-aware workflows that are easier to guide, analyze, and close. Together, these approaches bring intelligence not only to individual verification engines, but also to the engineering decisions that connect them.
Rather than treating AI as a chatbot or isolated code generator, agentic verification connects design intent, verification plans, RTL, testbenches, assertions, coverage, logs, waveforms, and engine results so AI can help engineers plan, execute, analyze, and refine verification tasks under human-defined governance. The session will examine the foundations required for trusted AI-driven flows, including domain-scoped agents, tool-aware context, persistent verification knowledge, traceability from recommendation to evidence, and orchestration across simulation, formal, emulation, and hardware-assisted validation.
Attendees will leave with a framework for identifying where AI can deliver near-term productivity gains inside verification tools, where agentic workflows can reduce friction across iterations, and how engineering judgment remains central to trusted signoff.
Presenter
Event Type
Exhibitor Forum
TimeMonday, July 2712:00pm - 12:30pm PDT
LocationExhibitor Forum, Exhibit Floor