Efficient embedded learning for IoT devices
Swagath Venkataramani, Kaushik Roy, Anand Raghunathan · 2016
The pervasiveness of IoT devices will usher an unprecedented growth in the amount of digital data produced and consumed. Realizing the rich class of applications enabled by IoT devices requires large-scale machine learning systems to analyze, organize and draw inferences from data. State-of-the-art machine learning algorithms are highly compute and data intensive, posing significant computational challenges across the spectrum of computing devices, from low-power client devices to the cloud. As benefits due to semiconductor technology scaling diminish, addressing the computational gap requires identifying new sources of computing efficiency. We highlight 3 approaches viz. machine learning accelerators, approximate computing and post-CMOS technologies that demonstrate significant promise in bridging the efficiency gap. Such technologies may be instrumental in enabling machine learning based IoT applications to enter the mainstream.