Modern edge devices, such as cameras, drones, and Internet-of-Things nodes,
rely on deep learning to enable a wide range of intelligent applications,
including object recognition, environment perception, and autonomous
navigation. Cependant, deploying deep learning models directly on the often
resource-constrained edge devices demands significant memory footprints and
computational power for real-time inference using traditional digital computing
architectures. In this paper, we present WISE, a novel computing architecture
for wireless edge networks designed to overcome energy constraints in deep
learning inference. WISE achieves this goal through two key innovations:
disaggregated model access via wireless broadcasting and in-physics computation
of general complex-valued matrix-vector multiplications directly at radio
frequency. Using a software-defined radio platform with wirelessly broadcast
model weights over the air, we demonstrate that WISE achieves 95.7% image
classification accuracy with ultra-low operation power of 6.0 fJ/MAC per
client, corresponding to a computation efficiency of 165.8 TOPS/W. This
approach enables energy-efficient deep learning inference on wirelessly
connected edge devices, achieving more than two orders of magnitude improvement
in efficiency compared to traditional digital computing.

Cet article explore les excursions dans le temps et leurs implications.

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