Measuring and Modeling the Power Consumption of Energy-Efficient FPGA Coprocessors for GEMM and FFT

Giefers, Heiner; Polig, Raphael; Hagleitner, Christoph

doi:10.1007/s11265-015-1057-6

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Both classifiers are built with the same hierarchical design principles, but the NRL network could not be implemented on the PYNQ-Z1 board due to insufficient available memory to hold the parameters and intermediate data representations. In order to estimate energy per classification, consumption was assumed to scale linearly with the number of multiply-accumulate (MAC) operations per inference, with 170 pJ per MAC assumed for an energy-efficient FPGA [20]. Latency and throughput estimates are derived directly from Vivado HLS compilation reports.…”

Section: Metricmentioning

confidence: 99%

A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge

Peng,

Lederman,

et al. 2021

Preprint

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Machine learning (ML) methods are ubiquitous in wireless communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and cognitive radio. However, the large size of ML models can make them difficult to implement on edge devices for latency-sensitive downstream tasks. In wireless communication systems, ML data processing at a sub-millisecond scale will enable real-time network monitoring to improve security and prevent infiltration. In addition, compact and integratable hardware platforms which can implement ML models at the chip scale will find much broader application to wireless communication networks. Toward real-time wireless signal classification at the edge, we propose a novel compact deep network that consists of a photonic-hardware-inspired recurrent neural network model in combination with a simplified convolutional classifier, and we demonstrate its application to the identification of RF emitters by their random transmissions. With the proposed model, we achieve 96.32% classification accuracy over a set of 30 identical ZigBee devices when using 50 times fewer training parameters than an existing state-of-the-art CNN classifier. Thanks to the large reduction in network size, we demonstrate real-time RF fingerprinting with 219 µs latency using a small-scale FPGA board, the PYNQ-Z1.

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Section: Metricmentioning

confidence: 99%