TVM性能评估分析(五)

TVM性能评估分析(五)

 TVM性能评估分析(五)

 

 TVM性能评估分析(五)

 

 TVM性能评估分析(五)

 

 Figure 3.  A futher speed up with operator fusion

 TVM性能评估分析(五)

 

 Table 1.  Performance issue of cuBLAS’ batch matmul

 TVM性能评估分析(五)

 

 Table 2.  Finding the best combination of number_thread. The results are obtained on a NVIDIA M40 GPU device with CUDA8.0.

 TVM性能评估分析(五)

 

 Figure 4.  DLPack provides an intermediate wrapper that is shared between frameworks and TVM

 TVM性能评估分析(五)

 

 Figure 5.  The OpenGL/WebGL Backend

 TVM性能评估分析(五)

 

 Figure 6. TVM utilizes a unified AST to define kernels, and compiles it to code on different platforms.

 TVM性能评估分析(五)

 

 Figure 7.  The benchmark is run in 4 different settings

 TVM性能评估分析(五)

 

 Figure 8. Inference Speed of Different Backends on ImageNet

 TVM性能评估分析(五)

 

 Figure 9.  Mali T860 and T880

 TVM性能评估分析(五)

 

 Figure 10.  Inference Speed of Different Backends on ImageNet

 TVM性能评估分析(五)

 

 Table 3. Inference Speed of FP16 on ImageNet

 

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