nvidia deep learning hardware
DEEP LEARNING Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation, and others. With deep neural networks becoming more complex, training times have increased dramatically, resulting in lower productivity and higher costs. Perhaps the most interesting hardware feature of the V100 GPU in the context of deep learning is its Tensor Cores. Deep learning researchers and framework developers worldwide rely on cuDNN for cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. Several Tier I organisations like Intel, Nvidia and Alibaba, among others, are striving hard to bridge the gap between the software and hardware. NVIDIA cuDNN The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. Exxact's deep learning infrastructure technology featuring NVIDIA GPUs significantly accelerates AI training, resulting in deeper insights in less time, significant cost savings, and faster time to ROI. These are specialised cores that can compute a 4×4 matrix multiplication in half-precision and accumulate the result to a single-precision (or half-precision) 4×4 matrix – … With the increasing demand in deep learning, the demand for better as well as sophisticated hardware has also increased. Deep learning differs from traditional machine learning techniques in that they can automatically learn representations from data such
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