  Convolutional neural networks are paramount in image and signal processing
including the relevant classification and training tasks alike and constitute
for the majority of machine learning compute demand today. With convolution
operations being computationally intensive, next generation hardware
accelerators need to offer parallelization and algorithmic-hardware
homomorphism. Fortunately, diffractive display optics is capable of
million-channel parallel data processing at low latency, however, thus far only
showed tens of Hertz slow single image and kernel capability, thereby
significantly underdelivering from its performance potential. Here, we
demonstrate an operation-parallelized high-throughput Fourier optic
convolutional neural network accelerator. For the first time simultaneously
processing of multiple kernels in Fourier domain enabled by optical diffraction
has been achieved alongside with already conventional in the field input
parallelism. Additionally, we show an about one hundred times system speed up
over existing optical diffraction-based processors and this demonstration
rivals performance of modern electronic solutions. Therefore, this system is
capable of processing large-scale matrices about ten times faster than state of
art electronic systems.
