Emmanuel Botte

About

Emmanuel Botte is a leading figure in the design of ultra-low-power artificial intelligence systems, with a focus on AI-enabled Internet-of-Things (AI-IoT) system-on-chip (SoC) architectures. His work addresses the critical challenge of running compute-intensive deep neural networks (DNNs) within power envelopes of just a few tens of milliwatts—essential for applications like augmented reality, personalized healthcare, and nano-robotics. Botte’s most cited paper, “A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing” (2023, 34 citations), exemplifies his contributions. This work introduces a precision-scalable DNN accelerator that dynamically adjusts bit precision from 2 to 8 bits, achieving an impressive 12.4 TOPS/W efficiency while integrating 16 RISC-V cores and adaptive body biasing for a 30% performance boost. His innovations are pivotal for enabling real-time, energy-efficient AI at the edge, making him a key contributor to the next generation of intelligent, low-power devices.

Research Focus

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Top Papers

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   22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing

   34 citations · 2023

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