Optimization of Spiking Neural Networks for Radar Applications by Muhammad Arsalan

Author:Muhammad Arsalan
Language: eng
Format: epub
ISBN: 9783658453183
Publisher: Springer Fachmedien Wiesbaden

(3.22)

where denotes the energy consumed per classification, represents the maximum number of spikes generated during classification, is the energy per spike, denotes the static leakage power, and represents the inference time. Assuming an inference time of , the energy consumption per classification of our proposed system is .

The proposed SNN system is characterized by its simplicity and effectiveness, demonstrating high accuracy in real-time hand gesture recognition with low latency, comparable to deepNets counterparts. Furthermore, SNNs exhibit energy efficiency by consuming power only when their corresponding neurons fire, which is inherently sparse. This feature positions them as an ideal solution for low-power hardware implementation, making them well-suited for consumer applications.

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