SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration

• URL: http://arxiv.org/abs/2304.13726v1
• Date: Tue, 11 Apr 2023 08:52:48 GMT
• Title: SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration
• Authors: Ivan Miro-Panades (LSTA), Benoit Tain (LECA), Jean-Frederic Christmann (LFIM), David Coriat (LIIM), Romain Lemaire (LIIM), Clement Jany, Baudouin Martineau (DSYS), Fabrice Chaix (DSYS), Guillaume Waltener (DSYS), Emmanuel Pluchart (LSTA), Jean-Philippe Noel (LFIM), Adam Makosiej, Maxime Montoya, Simone Bacles-Min (LIIM), David Briand (LIAE), Jean-Marc Philippe, Yvain Thonnart (LFIM), Alexandre Valentian (LSTA), Frederic Heitzmann (DSYS), Fabien Clermidy (DSCIN)
• Abstract summary: This paper presents SamurAI, a versatile IoT node bridging this gap in processing and in energy by leveraging two on-chip sub-systems. AR contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207ns wake-up time optimized for sporadic computing. OD combines a deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex tasks up to 36GOPS.
• Score: 37.89976990030855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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