Accelerating Edge Intelligence: Challenges and Future Directions in Hardware-Driven Machine Learning

The joining of hardware speed-up for machine learning (ML) in small devices shows a very important step in technology growth.This ability to run complicated ML models on low-power systems opens up many uses, like Internet of Things (IoT) gadgets, robots and different edge computing tasks.This paper studies the present condition of hardware speed-up for ML in small devices, clarifies difficulties and looks at future paths for progress.