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RUNTIME OPTIMIZATION OF COMPUTATIONS OF AN ARTIFICIAL NEURAL NETWORK COMPILED FOR EXECUTION ON A DEEP LEARNING ACCELERATOR
RUNTIME OPTIMIZATION OF COMPUTATIONS OF AN ARTIFICIAL NEURAL NETWORK COMPILED FOR EXECUTION ON A DEEP LEARNING ACCELERATOR
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机译:为在深度学习加速器上执行而编译的人工神经网络计算的运行时优化
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摘要
Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with random access memory (RAM). A compiler is configured to generate instructions executable by the Deep Learning Accelerator from a description of a target artificial neural network. The instructions may call routines in a runtime library that has an embedded artificial neural network configured to predict optimized execution options available to implement the routines. The prediction is based at least in part on a pattern of data being processed in the target artificial neural network and/or a pattern of usages of the routines by the instructions.
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