Bringing deep learning to IoT devices

Deep learning is well known for solving seemingly intractable problems in computer vision and natural language processing, but it typically does so by using massive CPU and GPU resources. Traditional deep learning techniques aren't well suited to addressing the challenges of Internet of Things (IoT) applications, however, because they can't apply the same level of computational resources. When running deep learning analysis on mobile devices, developers must adapt to a more resource-constrained platform. Image analysis on resource-constrained platforms can consume significant compute and memory resources. For example, the SpotGarbage app uses convolutional neural networks to detect garbage in images but consumes 83 percent of CPU and takes more than five seconds to respond. Fortunately, recent advances in network compression, approximate computing, and accelerators are enabling deep learning on resource-constrained IoT devices.