SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration
Increased capabilities such as recognition and self-adaptability are now
required from IoT applications. While IoT node power consumption is a major
concern for these applications, cloud-based processing is becoming
unsustainable due to continuous sensor or image data transmission over the
wireless network. Thus optimized ML capabilities and data transfers should be
integrated in the IoT node. Moreover, IoT applications are torn between
sporadic data-logging and energy-hungry data processing (e.g. image
classification). Thus, the versatility of the node is key in addressing this
wide diversity of energy and processing needs. This paper presents SamurAI, a
versatile IoT node bridging this gap in processing and in energy by leveraging
two on-chip sub-systems: a low power, clock-less, event-driven
Always-Responsive (AR) part and an energy-efficient On-Demand (OD) part. AR
contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a
207ns wake-up time optimized for sporadic computing, while OD combines a
deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex
tasks up to 36GOPS. This architecture partitioning achieves best in class
versatility metrics such as peak performance to idle power ratio. On an
applicative classification scenario, it demonstrates system power gains, up to
3.5x compared to cloud-based processing, and thus extended battery lifetime.