Reducing memory of neural networks for IoT/edge devices
Video analytics solutions are typically a mix of multifaceted applications such as drones, robots, self-driving cars, safety surveillance systems etc. all of which require on-edge computation. Especially, with the rise of IoT solutions, there is need of lower memory AI systems on edge devices.
To accommodate this requirement, we developed an end-to-end automatic neural network memory reduction technology called “Multilayer Pruning Framework” that focuses on reducing memory and computation. We further validated the technology developed on multiple video analytics applications involving object detection and image classification to achieve up to 96% and 90% reduction in memory and computation, respectively.
