CLUE: Cross-Layer Uncertainty Estimator for Reliable Neural Perception using Processing-in-Memory Accelerators

Minah Lee, Anni Lu, Mandovi Mukherjee, Shimeng Yu, Saibal Mukhopadhyay

Abstract: One of the primary challenges of deploying deep neural networks (DNNs) is ensuring their reliable performance in unpredictable edge environments, which are often disrupted by a variety of uncertainties and variations. Estimating uncertainty is crucial in order to understand the reliability of task predictions and prevent system failures. However, quantifying uncertainty stemming from non-ideal properties of processing hardware has not yet been thoroughly studied. To address this, we present Cross-Layer Uncertainty Estimator (CLUE), which quantifies task uncertainty originating from both sensing/processing hardware variations and DNN algorithm uncertainty. Our experimental results demonstrate that CLUE provides uncertainty with up to 80.4% less calibration error and only 12% of energy overheads compared to using task DNN solely. Furthermore, CLUE is able to detect unreliable tasks that stem from processing hardware variations, which prior uncertainty estimators were unable to achieve. Finally, we demonstrate an adaptive control of processing hardware using CLUE, which allows a dynamic trade-off control between task accuracy and energy consumption.