Speaker
Description
When deployed in edge applications, neural networks (NNs) undergo numerous changes to ensure they adhere to strict power, performance, and size constraints while simultaneously being robust to faults. In prior work, NN robustness is evaluated using a bit-level ranking based on how sensitive an edge NN is to a fault in a given parameter bit. Unfortunately, the fault injection campaigns used to generate such sensitivity rankings are extremely time-consuming.
SliceFI is a fault injection tool that enables efficient NN robustness analysis by performing a reduced number of model computations per fault injection. Prior to fault injection, SliceFI creates layer-wise model “slices” and then caches the expected outputs for each slice. During fault injection, SliceFI determines the sensitivity of a bit using a statistical heuristic involving the deviation from its slice’s expected output. By not recomputing upstream or downstream slices, SliceFI allows designers to rapidly produce bit-level sensitivity rankings during the edge NN design process.
