《DaDianNao: A Machine-Learning Supercomputer》

原创已于 2022-04-18 21:47:03 修改 · 658 阅读

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于 2022-02-28 22:49:26 首次发布

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本文探讨了DaDianNao在神经网络处理中，相比于GPU的450.65倍速度提升和150.31%的能耗降低，尤其是在64芯片系统上。着重分析了GPU的高成本、执行时间长和能源效率一般的问题，并对比了使用加速器的3mm²、65nm工艺的高效解决方案。

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DaDianNao:机器学习超级计算机 – MICRO2014

Abstract ：

We show that, on a subset of the largest known neural network layers, it
is possible to achieve a speedup of 450.65x over a GPU, and
reduce the energy by 150.31x on average for a 64-chip system.

III. THE GPU OPTION

1）their (area) cost is high because of both the number of hardware operators and the need to remain reasonably general-purpose.
2) Second, the total execution time remains large (up to 18.03
seconds for the largest layer CLASS1)
3) Third, the GPU energy efficiency is moderate.