Reducing Overhead of Feature Importance Visualization via Static GradCAM Computation

Abstract

Explainable AI (XAI) methods provide insights into the operation of black-box Deep Neural Network (DNN) models. GradCAM, an XAI algorithm, provides an explanation by highlighting regions in the input feature space that were relevant to the model’s output. It involves a gradient computation step that adds a significant overhead compared to inference and hinders providing explanations to end-users. In this work, we identify the root cause of the problem to be the dynamic run-time automatic differentiation. To overcome this issue, we propose to offload the gradient computation step to compile time via analytic evaluation. We validate the idea by designing an FPGA implementation of GradCAM that schedules the entire computation graph statically. For a TinyML ResNet18 model, we achieve a reduction in the explanation generation overhead from > 2× using software frameworks on CPU/GPU systems to < 0.01× on the FPGA using our designed hardware and static scheduling.