The application discloses an end-side
large model architecture
analysis method and
system based on an electromagnetic side channel, and belongs to the technical field of
artificial intelligence security. Firstly,
electromagnetic radiation signals generated when a target end-side device runs a
large model are collected and converted into digitized samples, and the samples are preprocessed to extract time-domain amplitude data reflecting changes in hardware computing load; then, based on the periodic repetition mode in the time-domain amplitude data, a target time-domain feature segment corresponding to the operation of a single
Transformer Block is segmented; finally, the feature segment is input into an architecture analysis model to identify the
execution time length features of each computing operator inside the segment, and the architecture hyperparameters of the
large model to be analyzed are predicted based on the
execution time length features. The application utilizes the
physical mapping relationship between hardware electromagnetic features and operator time length, breaks through the
software black box limitation, realizes non-intrusive and high-precision
model architecture analysis, and effectively solves the large model infringement
evidence collection problem.