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GTC ON-DEMAND

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Abstract:

We'll discuss an implementation of GPU convolution that favors coalesced accesses without requiring prior data transformations. Convolutions are the core operation of deep learning applications based on convolutional neural networks. Current GPU architectures are typically used for training deep CNNs, but some state-of-the-art implementations are inefficient for some commonly used network configurations. We'll discuss experiments that used our new implementation, which yielded notable performance improvements including up to 2.29X speedups in a wide range of common CNN configurations.

We'll discuss an implementation of GPU convolution that favors coalesced accesses without requiring prior data transformations. Convolutions are the core operation of deep learning applications based on convolutional neural networks. Current GPU architectures are typically used for training deep CNNs, but some state-of-the-art implementations are inefficient for some commonly used network configurations. We'll discuss experiments that used our new implementation, which yielded notable performance improvements including up to 2.29X speedups in a wide range of common CNN configurations.

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Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9218
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