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A team of computer scientists have developed a RISC-V-based platform for embedded machine learning workloads — and say it offers 65 times the performance and 37 times the energy efficiency of the same workload running on an STMicroelectronics STM32 microcontroller.

“In the last few years, research and development on deep learning models and techniques for ultra-low-power devices — in a word, tinyML — has mainly focused on a train-then-deploy assumption,” the researchers explain in the abstract to their paper, “with static models that cannot be adapted to newly collected data without cloud-based data collection and fine-tuning.”

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