Artificial Neural Network Accelerator for Classification of In-Field Conducted Noise in Integrated Circuits' DC Power Lines

With the growing use of embedded systems in our daily lives and the increasing electromagnetic noise level in the environment in which these systems are exposed, the need for reliable operation is paramount. In this scenario, this work presents a study on the use of Artificial Neural Networks (ANNs) to perform in-field identification and classification of different types of noise conducted in the integrated circuit (IC) DC power lines according to a specific set of IEC standards. After identification, proactive actions can be taken to guarantee the expected IC robustness. Such actions can be, for instance, slow down IC clock frequency, increasing power supply voltage and/or activating error detection & correction (EDAC) functions during the period the system is operating under such noise exposition. Experimental results demonstrate that the ANN was able to identify and classify different types of conducted noise in power supply lines with a success rate ranging from 70 to 100% within a latency in the order of 995 ns.