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Computer vision (CV), a form of artificial intelligence (AI), is a foundational technology within the automotive industry for an increasing number of applications including active safety, motion control, and driver distraction monitoring. State-of-the-art CV models often rely on the use of Deep Neural Networks (DNNs) to achieve high levels of accuracy. While necessary for their accuracy, DNNs are computationally complex. For example, when compared to other AI model architectures, they have a large memory footprint and often require billions of floating-point operations to create an output or prediction. To meet performance goals in the face of such constraints, high performance processors such as Graphics Processing Units (GPUs) are typically required to run CV models on-board automobiles, creating a major hurdle to the deployment of CV applications.

Exhaust temperature models are widely used in the automotive industry to estimate catalyst and exhaust gas temperatures and to protect the catalyst and other vehicle hardware against over-temperature conditions. Modeled exhaust temperatures rely on air, fuel, and spark measurements to make their estimate. Errors in any of these measurements can have a large impact on the accuracy of the model. Furthermore, air-fuel imbalances, air leaks, engine coolant temperature (ECT) or air charge temperature (ACT) inaccuracies, or any unforeseen source of heat entering the exhaust may have a large impact on the accuracy of the modeled estimate. Modern universal exhaust gas oxygen (UEGO) sensors have heaters with controllers to precisely regulate the oxygen sensing element temperature. These controllers are duty cycle based and supply more or less current to the heating element depending on the temperature of the surrounding exhaust gas.