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This paper introduces an optoelectronic architecture leveraging an opto-atomic Spatio-temporal Holographic Correlator (STHC) to accelerate 3D CNNs for video recognition. The STHC stores temporal information as atomic coherence in cold Rubidium-85 atoms, enabling simultaneous spatio-temporal correlation. Experiments on a four-class human action dataset achieved 59.72% accuracy with large kernels, projecting potential speeds of 125,000 frames per second.
Ditch silicon bottlenecks: a novel optoelectronic correlator uses cold atoms to accelerate 3D CNNs by orders of magnitude.
Three-dimensional convolutional neural networks (3D CNNs) have demonstrated remarkable performance in video recognition tasks by processing both spatial and temporal features. However, the cubic scaling of computational complexity poses significant time and energy efficiency challenges for conventional silicon-based hardware. To address this, we propose a hybrid optoelectronic architecture that delegates the computationally intensive 3D convolutional layer to an opto-atomic Spatio-temporal Holographic Correlator (STHC). This system stores temporal information as atomic coherence in an array of inhomogeneously broadened cold Rubidium-85 atoms and combines a traditional 2D spatial correlator to perform correlation in both space and time simultaneously. Our results on a four-class human action dataset demonstrate a classification accuracy of 59.72% using parallel large-scale kernels (30X40 pixels spatially, 8 frames temporally), with potential operating speeds projected up to 125,000 frames per second. This approach offers a pathway to massively accelerated video classification through a hybrid architecture.
