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20172026
most citedPULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors

136 citations · 266 across the 18 of their papers we have counts for

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cs.CV2025

Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUs

Davide Nadalini, Manuele Rusci, Elia Cereda +3

Monocular depth estimation (MDE) plays a crucial role in enabling spatially-aware applications in Ultra-low-power (ULP) Internet-of-Things (IoT) platforms. However, the limited num…