Tesla deep learning




Tesla deep learning

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  • This would give Tesla a huge advantage in the present, at the risk of being unable to leverage any new deep learning breakthroughs.

    2 days ago This article takes a look at the latest updates to the Deep Learning networks behind autopilot in Tesla cars. Explore how it has impressed the.

    Tesla has hired deep learning and computer vision expert Andrej Karpathy in a key Autopilot role. Karpathy most recently held a role as a.

    Tesla deep learning

    Tesla deep learning

    As a simple proof of concept, only these two SUV types were included in the data set. Knowledge Center Resources Support. Each is configured with GB of system memory and dual core Intel Xeon Ev4 processors with a base frequency of 2. CPU times are also averaged geometrically across framework type.

    Tesla deep learning

    Tesla deep learning

    Tesla deep learning

    Tesla deep learning

    Tesla deep learning

    How Tesla's autopilot learns | Fortune

    Sources of CPU benchmarks, used for estimating performance on similar workloads, have been available throughout the course of CPU development. For example, the Standard Performance Evaluation Corporation has compiled a large set of applications benchmarks, running on a variety of CPUs, across a multitude of systems.

    There are certainly benchmarks for GPUs, but only during the past year has an organized set of deep learning benchmarks been published. Called DeepMarks, these deep learning benchmarks are available to all developers who want to get a sense of how their application might perform across various deep learning frameworks. The benchmarking scripts used for the DeepMarks study are published at GitHub. The deep learning frameworks covered in this benchmark study are TensorFlow, Caffe, Torch, and Theano.

    Tesla deep learning

    All deep learning benchmarks were single-GPU runs. The benchmarking scripts used in this study are the same as those found at DeepMarks. DeepMarks runs a series of benchmarking scripts which report the time required for a framework to process one forward propagation step, plus one backpropagation step. The sum of both comprises one training iteration. The times reported are the times required for one training iteration per batch, in milliseconds. To start, we ran CPU-only trainings of each neural network.

    We then ran the same trainings on each type of GPU. The plot below depicts the ranges of speedup that were obtained via GPU acceleration. GPU speedup ranges over CPU-only trainings — geometrically averaged across all four framework types and all four neural network types. If we expand the plot and show the speedups for the different types of neural networks, we see that some types of networks undergo a larger speedup than others.

    Tesla deep learning

    GPU speedups over CPU-only trainings — geometrically averaged across all four deep learning frameworks. The speedup ranges from Figure 1 are uncollapsed into values for each neural network architecture. If we take a step back and look at the ranges of speedups the GPUs provide, there is a fairly wide range of speedup.

    The plot below shows the full range of speedups measured without geometrically averaging across the various deep learning frameworks. Note that the ranges are widened and become overlapped. Speedup factor ranges without geometric averaging across frameworks. We believe the ranges resulting from geometric averaging across frameworks as shown in Figure 1 results in narrower distributions and appears to be a more accurate quality measure than is shown in Figure 3.

    However, it is instructive to expand the plot from Figure 3 to show each deep learning framework. Those ranges, as shown below, demonstrate that your neural network training time will strongly depend upon which deep learning framework you select.

    Tesla deep learning

    GPU speedups over CPU-only trainings — showing the range of speedups when training four neural network types. The speedup ranges from Figure 3 are uncollapsed into values for each deep learning framework. With that in mind, the plot below shows the raw training times for each type of neural network on each of the four deep learning frameworks.

    We provide more discussion below. For reference, we have listed the measurements from each set of tests. Times reported are in msec per batch. The batch size for all training iterations measured for runtime in this study is , except for VGG net, which uses a batch size of When geometric averaging is applied across framework runtimes, a range of speedup values is derived for each GPU, as shown in Figure 1.

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