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and in V12 is a framework for boosting DN and cold inference on devices due to limitations in Hardware resources the DNN inference often occurs in a cold manner on device DNN cold inference could occur in both active and passive manners active cold inference happens for developers willingness passive cold inference happens off of the control of Developers unfortunately the status art DN engines are not ready to boost cold inference cold inference mainly includes the following stage weights reading weights transformation model execution how to speed up cold inference we first discuss the optimization knobs kernel selection the kernels represent the different implementation of an operator the current kernel selection policy of popular BNN engines is hard-coded and only considers the warm inference speed bypass weights transformation our method to avoid the heavy transformation is to Cache the transformed weights on disk which can be directly loaded and executed pipeline inference weights