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This representation does have certain limitations. Given sufficient graphics processing power even graphics programmers would like to use better formats, such as floating point data formats, to obtain effects such as high-dynamic-range imaging. Many GPGPU applications require floating point accuracy, which came with video cards conforming to ...
Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5] The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]
In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. [30] In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be ...
"Tensor" is a reference to Google's TensorFlow and Tensor Processing Unit technologies, and the chip is developed by the Google Silicon team housed within the company's hardware division, led by vice president and general manager Phil Carmack alongside senior director Monika Gupta, [15] in conjunction with the Google Research division.
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
The software implements any number of layers of non-linear processing units for supervised learning.This deep architecture allows the design of neural networks with universal approximation properties.
ROCm is free, libre and open-source software (except the GPU firmware blobs [4]), and it is distributed under various licenses. ROCm initially stood for Radeon Open Compute platfor m ; however, due to Open Compute being a registered trademark, ROCm is no longer an acronym — it is simply AMD's open-source stack designed for GPU compute.
RK3399 was the flagship SoC of Rockchip, Dual A72 and Quad A53 and Mali-T860MP4 GPU, providing high computing and multi-media performance, rich interfaces and peripherals. And software supports multiple APIs: OpenGL ES 3.2, Vulkan 1.0, OpenCL 1.1/1.2, OpenVX1.0, AI interfaces support TensorFlow Lite/AndroidNN API. [1]