Leveraging Stochastic Depth Training for Adaptive Inference
This work addresses efficiency and adaptability challenges in deep neural networks for applications requiring real-time or resource-constrained inference, offering a simpler alternative to existing dynamic optimization techniques.
The paper tackles the problem of dynamic DNN optimization for adaptive inference by proposing a method that leverages Stochastic Depth training to enable efficient layer-skipping, resulting in up to 2X improvements in power efficiency with minimal accuracy drops as low as 0.71%.
Dynamic DNN optimization techniques such as layer-skipping offer increased adaptability and efficiency gains but can lead to i) a larger memory footprint as in decision gates, ii) increased training complexity (e.g., with non-differentiable operations), and iii) less control over performance-quality trade-offs due to its inherent input-dependent execution. To approach these issues, we propose a simpler yet effective alternative for adaptive inference with a zero-overhead, single-model, and time-predictable inference. Central to our approach is the observation that models trained with Stochastic Depth -- a method for faster training of residual networks -- become more resilient to arbitrary layer-skipping at inference time. We propose a method to first select near Pareto-optimal skipping configurations from a stochastically-trained model to adapt the inference at runtime later. Compared to original ResNets, our method shows improvements of up to 2X in power efficiency at accuracy drops as low as 0.71%.