Pete Warden, Staff Research Engineer and TensorFlow Lite development lead at Google, presents the "Future of Computer Vision and Machine Learning is Tiny" tutorial at the May 2019 Embedded Vision Summit.
There are 150 billion embedded processors in the world — more than twenty for every person on earth — and this number grows by 20% each year. Imagine a world in which these hundreds of billions of devices not only collect data, but transform that data into actionable insights — insights that in turn can improve the lives of billions of people.
To do this, we need machine learning, which has radically transformed our ability to extract meaningful information from noisy data. But conventional wisdom is that machine learning consumes a vast amount of processing performance and memory — which is why today you find it mainly in the cloud and in high-end embedded systems. What if we could change that? What would it take to do that, and what would that world look like?
In this talk, Warden shares his unique perspective on the state of the art and future of low-power, low-cost machine learning. He highlights some of the most advanced examples of current machine learning technology and applications, which give some intriguing hints about what the future holds. He also explores the ability of convolutional neural networks to handle a surprisingly diverse array of tasks, ranging from image understanding to speech recognition to malware detection. Looking forward, Warden shares his vision for the opportunities being opened up by this transformative technology, examines the key challenges that remain to be overcome and presents his call to action for developers to make this vision a reality.
