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arXiv 2102.00932cs.ARcs.AIcs.LGcs.PF
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Understanding Cache Boundness of ML Operators on ARM Processors

Heidelberg University(海德堡大学);Materials Center Leoben(莱奥本材料中心)

Bernhard Klein, Christoph Gratl, Manfred Mücke, Holger Fröning

英文摘要:

Machine Learning compilers like TVM allow a fast and flexible deployment on embedded CPUs. This enables the use of non-standard operators, which are common in ML compression techniques. However, it is necessary to understand the limitations of typical compute-intense operators in ML workloads to design a proper solution. This is the first in-detail analysis of dense and convolution operators, generated with TVM, that compares to the fundamental hardware limits of embedded ARM processors. Thereby it explains the gap between computational peak performance, theoretical and measured, and real-world state-of-the-art results, created with TVM and openBLAS. Instead, one can see that single-precision general matrix multiply (GEMM) and convolutions are bound by L1-cache-read bandwidth. Explorations of 8-bit and bit-serial quantized operators show that quantization can be used to achieve relevant speedups compared to cache-bound floating-point operators. However, the performance of quantized operators highly depends on the interaction between data layout and bit packing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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