Bar Plot from Scientific Research

A breakdown of the estimated OPs (red bars) and the associated energy consumption (light-blue bars for a state-of-the-art GPU; dark-blue bars for a projected random resistive memory-based hybrid analogue–digital system). In a forward (backward) pass, the fully trainable model on a state-of-the-art GPU and ESGNN on a projected random resistive memory-based hybrid analogue–digital system consume approximately 24.20 mJ (approximately 63.18 GOPs) and approximately 599.47 uJ (approximately 5.43 GOPs), respectively, revealing a >40.37 fold improvement in the inference energy efficiency (and an approximately 91.40% reduction of the backward pass complexity).

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