Talk to microchips, i.e. end-to-end automatic spoken language understanding (SLU) for microcontrollers and microchips, while advocating for climate-friendly solutions through low-power consumption AI models.The talk would be geared towards data scientists who are interested more in learning about how audio is processed, and how models are trained with it. They will specifically gain insights on challenges faced when working with audio. From data pre-processing, to model architectures and evaluation metrics.The implications of this research extend to businesses and researchers interested in enhancing human-machine interaction and accessibility. Industries such as consumer electronics, healthcare, automotive, and education stand to benefit from our SLU solution's transformative capabilities. Furthermore, our commitment to sustainability is demonstrated through reduced training time, low power consumption, and efficient resource utilization.