在 5.1 Performance Prediction Model中，提到的GFLOPS起到什么作用？模型的输入时各种层的配置参数，输出是层的执行时间吗？
5.1 Performance Prediction Model
Neurosurgeon models the per-layer latency and the energy consumption of arbitrary neural network architecture. This approach allows Neurosurgeon to estimate the latency and energy consumption of a DNN's constituent layers without executing the DNN.
We observe that for each layer type, there is a large latency variation across layer configurations. Thus, to construct the prediction model for each layer type, we vary the configurable parameters of the layer and measure the latency and power consumption for each configuration. Using these profiles, we establish a regression model for each layer type to predict the latency and power of the layer based on its configuration. We describe each layer's regression model variables later in this section. We use GFLOPS (Giga Floating Point Operations per Second) as our performance metric. Based on the layer type, we use either a logarithmic or linear function as the regression function. The logarithmic-based regression is used to model the performance plateau as the computation requirement of the layer approaches the limit of the available hardware resources.
最后一句当层的计算需求接近可用硬件资源的极限时，使用基于对数的回归对性能平台进行建模，我不太理解，大佬能解答一下吗
在 5.1 Performance Prediction Model中，提到的GFLOPS起到什么作用？模型的输入时各种层的配置参数，输出是层的执行时间吗？
5.1 Performance Prediction Model
Neurosurgeon models the per-layer latency and the energy consumption of arbitrary neural network architecture. This approach allows Neurosurgeon to estimate the latency and energy consumption of a DNN's constituent layers without executing the DNN.
We observe that for each layer type, there is a large latency variation across layer configurations. Thus, to construct the prediction model for each layer type, we vary the configurable parameters of the layer and measure the latency and power consumption for each configuration. Using these profiles, we establish a regression model for each layer type to predict the latency and power of the layer based on its configuration. We describe each layer's regression model variables later in this section. We use GFLOPS (Giga Floating Point Operations per Second) as our performance metric. Based on the layer type, we use either a logarithmic or linear function as the regression function. The logarithmic-based regression is used to model the performance plateau as the computation requirement of the layer approaches the limit of the available hardware resources.
最后一句当层的计算需求接近可用硬件资源的极限时，使用基于对数的回归对性能平台进行建模，我不太理解，大佬能解答一下吗