PZnet: Efficient 3D ConvNet Inference on Manycore CPUs | 0 | 0.34 | 2019 |
The anatomy of efficient FFT and winograd convolutions on modern CPUs | 1 | 0.35 | 2019 |
Optimizing N-dimensional, winograd-based convolution for manycore CPUs. | 3 | 0.39 | 2018 |
FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why. | 0 | 0.34 | 2018 |
Compile-time optimized and statically scheduled N-D convnet primitives for multi-core and many-core (Xeon Phi) CPUs. | 4 | 0.41 | 2017 |
Scalable training of 3D convolutional networks on multi- and many-cores. | 2 | 0.37 | 2017 |
A Multicore Path to Connectomics-on-Demand. | 2 | 0.45 | 2017 |
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs. | 0 | 0.34 | 2016 |
ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs. | 2 | 0.43 | 2016 |
Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph | 5 | 0.46 | 2015 |
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction. | 0 | 0.34 | 2015 |
ZNN -- A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-core and Many-Core Shared Memory Machines | 11 | 0.87 | 2015 |
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection | 9 | 0.58 | 2015 |