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. Tuttavia, 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.

Questo articolo esplora i giri e le loro implicazioni.

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