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FLOATING-POINT UNIT STOCHASTIC ROUNDING FOR ACCELERATED DEEP LEARNING
FLOATING-POINT UNIT STOCHASTIC ROUNDING FOR ACCELERATED DEEP LEARNING
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机译:浮点单元随机圆桌可加快深度学习
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摘要
Techniques in advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element has a respective compute element and a respective routing element. Each compute element has a respective floating-point unit enabled to perform stochastic rounding, thus in some circumstances enabling reducing systematic bias in long dependency chains of floating-point computations. The long dependency chains of floating-point computations are performed, e.g., to train a neural network or to perform inference with respect to a trained neural network.
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