Opto-Atomic Spatio-Temporal Holographic Correlators for High-Speed 3D CNNs
This work addresses the computational bottleneck of 3D CNNs for video recognition by introducing a novel hardware acceleration approach, though the accuracy is low and the dataset is small.
The authors propose a hybrid optoelectronic architecture using an opto-atomic spatio-temporal holographic correlator to accelerate 3D CNNs, achieving 59.72% accuracy on a four-class human action dataset with projected speeds up to 125,000 fps.
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.