Presentation
Multicore Environment State Representation for Agent-Directed Test Generation
DescriptionA crucial step in the design of multicore systems is to validate the interaction between
cores. This involves test program generation and runtime analysis. We propose a novel
reinforcement learning approach to directed test generation, where an agent induces a suite of
programs, which are executed in a simulation environment for a multicore. It focuses on how to
recover state information from raw observations of the environment such that the agent can learn
from interaction how to improve coverage for any verification task. We evaluated our state
representation for different verification tasks involving 16 and 32-core ARMv8 2-level MOESI
designs.
cores. This involves test program generation and runtime analysis. We propose a novel
reinforcement learning approach to directed test generation, where an agent induces a suite of
programs, which are executed in a simulation environment for a multicore. It focuses on how to
recover state information from raw observations of the environment such that the agent can learn
from interaction how to improve coverage for any verification task. We evaluated our state
representation for different verification tasks involving 16 and 32-core ARMv8 2-level MOESI
designs.
Event Type
Research Manuscript
TimeWednesday, June 253:45pm - 4:00pm PDT
Location3003, Level 3
EDA
EDA2: Design Verification and Validation