Whether this is your first time leading a deep learning (DL) project or you’ve been here before, you will encounter the same obstacles. Here is one of the challenges we list in our guide Overcome 4 main challenges when deploying deep learning in embedded systems.
You and your team have been working on a deep learning based AI feature – and it’s finally time to test it out on the target hardware. The deadline creeps closer, and you realize, “It’s not fast enough, and it doesn’t meet the real-time requirements: It only does X – not Y.” Your embedded system simply doesn’t have the computational power to handle the requests when you deploy it on your target hardware. And as the system grows with additional product features – the problem will worsen. The budget is tight, you’re running out of time, and it takes a tremendous amount of experimentation and crafting design to attempt to fix it. Your engineers would better spend that time actually solving your core problems.
Instead of putting in hours of manual labor to figure out what you can cut to meet the real-time requirements and reduce the latency, you should implement an automated optimization pipeline. It compresses and optimizes your neural network for a specific hardware target to reach predefined latency, memory and power requirements, giving you the flexibility to adapt to fast-changing external requirements on any hardware target. This method requires niched expert skills that not many DL engineers possess. Instead, look into providers that offer automated methods and tools that re-design the AI model to reduce the complexity, remove redundancy and find the optimal trade-off between accuracy and latency. This will allow your engineers to focus on core problems and shorten the time-to-market.