In this talk, we present alternative and more efficient methods of processing raw camera images using neural network accelerators. We begin by introducing Analog Devices’ convolutional neural network accelerator, MAX78000, and showing how it achieves superior performance and energy efficiency on a range of neural network inference tasks. In visual AI applications, cameras provide raw images not in the familiar RGB format, but in a Bayer format. In order to process these images using a neural network that was trained on RGB data, the camera images must be “de-Bayerized” to turn them into RGB images. The conventional way of performing this step is via interpolation. Unfortunately, this increases energy consumption and latency of the application since it cannot be performed by neural network accelerators. We present alternative methods of performing this task using neural network accelerators and demonstrate the effectiveness of these techniques.