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2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP) (2018)
Milano, Italy
July 10, 2018 to July 12, 2018
ISSN: 2160-052X
ISBN: 978-1-5386-7480-2
pp: 1-4
Eric Flamand , GreenWaves Technologies, Villard-Bonnot, France
Davide Rossi , GreenWaves Technologies, Villard-Bonnot, France
Francesco Conti , DEI, University of Bologna
Igor Loi , GreenWaves Technologies, Villard-Bonnot, France
Antonio Pullini , GreenWaves Technologies, Villard-Bonnot, France
Florent Rotenberg , GreenWaves Technologies, Villard-Bonnot, France
Luca Benini , DEI, University of Bologna
ABSTRACT
Current ultra-low power smart sensing edge devices, operating for years on small batteries, are limited to low-bandwidth sensors, such as temperature or pressure. Enabling the next generation of edge devices to process data from richer sensors such as image, video, audio, or multi-axial motion/vibration has huge application potential. However, edge processing of data-rich sensors poses the extreme challenge of squeezing the computational requirements of advanced, machine-Iearning-based near-sensor data analysis algorithms (such as Convolutional Neural Networks) within the mW-range power envelope of always-ON battery-powered IoT end-nodes. To address this challenge, we propose GAP-8: a multi-GOPS fully programmable RISC-V IoT-edge computing engine, featuring a 8-core cluster with CNN accelerator, coupled with an ultra-low power MCU with 30 μW state-retentive sleep power. GAP-8 delivers up to 10 GMAC/s for CNN inference (90 MHz, 1.0V) at the energy efficiency of 600 GMAC/s/W within a worst-case power envelope of75 mW.
INDEX TERMS
Kernel, Sensors, Energy efficiency, Computer architecture, Hardware, Data analysis, Convolution
CITATION

E. Flamand et al., "GAP-8: A RISC-V SoC for AI at the Edge of the IoT," 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP), Milano, Italy, 2018, pp. 1-4.
doi:10.1109/ASAP.2018.8445101
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