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The anatomy of efficient FFT and winograd convolutions on modern CPUs., , , and . ICS, page 414-424. ACM, (2019)Large-scale image segmentation based on distributed clustering algorithms., , and . CoRR, (2021)Scalable training of 3D convolutional networks on multi- and many-cores., , and . J. Parallel Distributed Comput., (2017)A Multicore Path to Connectomics-on-Demand., , , , , , , , and . PPoPP, page 267-281. ACM, (2017)Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction., , , and . NIPS, page 3573-3581. (2015)ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines., , and . CoRR, (2015)L3 Fusion: Fast Transformed Convolutions on CPUs., , and . CoRR, (2019)ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs., , and . SC, page 854-865. IEEE Computer Society, (2016)Scalable algorithms for semi-automatic segmentation of electron microscopy images of the brain tissue.. Massachusetts Institute of Technology, Cambridge, USA, (2016)ndltd.org (oai:dspace.mit.edu:1721.1/105955).ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs., , and . CoRR, (2016)