… neural networks. In the case of networks like CosmoFlow with multiple consecutive convolution
layers, the runtime for convolution layers … NVIDIA’s CUTLASS [29], for direct convolution …

… unit can help reduce the power load and memory usage on the GPU and … guide NVIDIA
explains that "DLA is designed to do full hardware acceleration on convolutional neural networks"…

… CPU approach can outperform the GPU hardware in certain … network structures such as
Convolutional Neural Networks (CNN)… of a convolution-based pose prediction model for use with …

… abstraction layer by … NVIDIA hardware, we compared GEMM function implementation in
the oneMKL open-source interfaces library with the native libraries provided by Intel and NVIDIA…

… Imagenet classification with deep convolutional neural networks. In Advances in Neural
Information Processing Systems. [20] Chris Lattner, Mehdi Amini, Uday Bondhugula, Albert …

… model using Tensor Core GPU. We set the configurations of V100 GPU according to the …
We use 2D convolution layers from ResNet-18 and compare the ground-truth performance …

… We use a latency cost constraint, defined by a layer-wise … speed on a NVIDIA TITAN V GPU
with cudNN V7.6.5 [4]. … pruning the early convolution layers and leaves the later layers better …

… We compare SMM-Conv to im2col and MEC with different convolutional layer parameters.
The … Yololite: a real-time object detection algorithm optimized for non-gpu computers. In 2018 …

… the GPU and TPU which is important for unpruned, dense models that continue to be used.
Any unstructured sparse operation … only in convolutional neural networks (CNNs). Recurrent …

… , convolution, the heart of convolutional neural networks (CNNs… differences between GEMM
and convolution, since most of … During this evaluation, we refer to an XPU as a GPU that uses …