Details

Autor(en) / Beteiligte

• Miro-Panades, Ivan
• Tain, Benoit
• Christmann, Jean-Frederic
• Coriat, David
• Lemaire, Romain
• Jany, Clement
• Martineau, Baudouin
• Chaix, Fabrice
• Waltener, Guillaume
• Pluchart, Emmanuel
• Noel, Jean-Philippe
• Makosiej, Adam
• Montoya, Maxime
• Bacles-Min, Simone
• Briand, David
• Philippe, Jean-Marc
• Thonnart, Yvain
• Valentian, Alexandre
• Heitzmann, Frederic
• Clermidy, Fabien

Titel

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

Ist Teil von

• IEEE journal of solid-state circuits, 2023-06, Vol.58 (6), p.1-0

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• Increased capabilities, such as recognition and self-adaptability, are now required from Internet-of-Things (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 into 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 article presents SamurAI, a versatile IoT node bridging this gap in processing and 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 1.7-MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207-ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3-TOPS/W machine learning (ML) for more complex tasks up to 36 GOPS. This architecture partitioning achieves the 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 3.5<inline-formula> <tex-math notation="LaTeX">\times</tex-math> </inline-formula>, compared to cloud-based processing, and, thus, extended battery lifetime.