Abstract
Training a large set of data takes GPU days using Deep convolution neural networks which are a time taking process. Self-driving cars require very low latency for pedestrian detection. Image recognition constrained by limited processing resources for mobile phones. The computation speed of the training set determines that in these situations convolution neural networks was a success. For large filters, Conventional Faster Fourier Transform based convolution is preferably fast, yet in case of small, 3 × 3 filters state of the art convolutional neural networks is used. By using Winograd's minimal filtering algorithms the new class of fast algorithms for convolutional neural networks was introduced by us. Instead of small tiles, minimal complexity convolution was computed by the algorithms, this increases the computing speed with small batch sizes and small filters. With the VGG network, we benchmark a GPU implementation of our algorithm and at batch sizes from 1 to 64 state of the art throughput was shown.
Highlights
Image recognition in state of the art results [1,2] is acquired by using deep convolution neural networks
Convergence of the network can be affected by large batch sizes adversely, so the upper limit on the cluster size was placed with the minimum group size can be computed efficiently
By using at most 16mb of workspace memory, and for all batch sizes the throughput is measured by Sate of art, from 1 to 64, was achieved by NVIDIA Maxwell Graphical Processing unit (GPU)
Summary
Image recognition in state of the art results [1,2] is acquired by using deep convolution neural networks (convnets). Several days of GPU time is taken for training in these networks and it requires significant compute resources during classification too. Likewise, when convnets are applied on low latency inference problems, such as to determine the how fast is tiny set of image data that can be determined and classified will limits the detection of people detection in autonomous cars in a video imagery. Distributed training of convnets(convolution neural networks) can be acquired and accumulating weight updates across the nodes. Convergence of the network can be affected by large batch sizes adversely, so the upper limit on the cluster size was placed with the minimum group size can be computed efficiently. By using at most 16mb of workspace memory, and for all batch sizes the throughput is measured by Sate of art , from 1 to 64, was achieved by NVIDIA Maxwell GPUs
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