Artificial neural network emulation of hotspots

Abstract

Methods, devices, and systems for emulating a compute kernel with an ANN. The compute kernel is executed on a processor, and it is determined whether the compute kernel is a hotspot kernel. If the compute kernel is a hotspot kernel, the compute kernel is emulated with an ANN, and the ANN is substituted for the compute kernel.

What is claimed is: 
     
         1 . A method for emulating a compute kernel with an artificial neural network (ANN), the method comprising:
 executing the compute kernel on a processor; and   substituting an ANN for the compute kernel.   
     
     
         2 . The method of  claim 1 , wherein the ANN is substituted for the compute kernel responsive to the compute kernel comprising a hotspot kernel. 
     
     
         3 . The method of  claim 1 , wherein the ANN is substituted for the compute kernel responsive to a compute resource utilization of the compute kernel exceeding a compute resource utilization of a different compute kernel, or exceeding a threshold. 
     
     
         4 . The method of  claim 1 , further comprising emulating the compute kernel with the ANN, wherein the ANN comprises an emulation of the compute kernel, and wherein emulating the compute kernel with the ANN comprises offline-training the ANN prior to executing the compute kernel. 
     
     
         5 . The method of  claim 4 , wherein offline-training the ANN comprises:
 inputting, to the ANN, training data typical of inputs to the compute kernel, comparing outputs from the ANN to known correct outputs corresponding to the training data; and adjusting the ANN based on differences between the outputs from the ANN and the known correct outputs.   
     
     
         6 . The method of  claim 1 , further comprising emulating the compute kernel with the ANN, wherein the ANN comprises an emulation of the compute kernel, and wherein emulating the compute kernel with the ANN comprises online-training the ANN based on execution of the compute kernel. 
     
     
         7 . The method of  claim 6 , wherein offline-training the ANN comprises:
 inputting, to the ANN, inputs which were input to the compute kernel, comparing outputs from the ANN to known correct outputs corresponding to the inputs which were input to the compute kernel; and adjusting the ANN based on differences between the outputs from the ANN and the known correct outputs.   
     
     
         8 . The method of  claim 1 , further comprising emulating the compute kernel with the ANN, wherein the ANN comprises an emulation of the compute kernel, and wherein emulating the compute kernel with the ANN comprises offline-training the ANN prior to executing the compute kernel, and refining the ANN using online-training based on execution of the compute kernel. 
     
     
         9 . The method of  claim 1 , wherein substituting the ANN for the compute kernel comprises executing the ANN on the processor. 
     
     
         10 . The method of  claim 1 , wherein substituting the ANN for the compute kernel comprises executing the ANN on a different processor in communication with the processor. 
     
     
         11 . A computing device configured to emulate a compute kernel with an artificial neural network (ANN), the computing device comprising:
 a processor configured to execute the compute kernel; and   the processor further configured to substitute an ANN for the compute kernel.   
     
     
         12 . The computing device of  claim 11 , wherein the processor is configured to substitute the ANN for the compute kernel responsive to the compute kernel comprising a hotspot kernel. 
     
     
         13 . The computing device of  claim 11 , wherein the processor is configured to substitute the ANN for the compute kernel responsive to a compute resource utilization of the compute kernel exceeding a compute resource utilization of a different compute kernel, or exceeding a threshold. 
     
     
         14 . The computing device of  claim 11 , wherein the ANN comprises an emulation of the compute kernel, and wherein emulating the compute kernel with the ANN comprises offline-training the ANN prior to executing the compute kernel. 
     
     
         15 . The computing device of  claim 14 , wherein offline-training the ANN comprises:
 inputting, to the ANN, training data typical of inputs to the compute kernel, comparing outputs from the ANN to known correct outputs corresponding to the training data; and   adjusting the ANN based on differences between the outputs from the ANN and the known correct outputs.   
     
     
         16 . The computing device of  claim 11 , wherein the ANN comprises an emulation of the compute kernel, and wherein emulating the compute kernel with the ANN comprises online-training the ANN based on execution of the compute kernel. 
     
     
         17 . The computing device of  claim 16 , wherein the processor is further configured to offline-train the ANN, the processor further configured to input, to the ANN, inputs which were input to the compute kernel, compare outputs from the ANN to known correct outputs corresponding to the inputs which were input to the compute kernel; and adjust the ANN based on differences between the outputs from the ANN and the known correct outputs. 
     
     
         18 . The computing device of  claim 11 , wherein the ANN comprises an emulation of the compute kernel, and wherein emulating the compute kernel with the ANN comprises offline-training the ANN prior to executing the compute kernel, and refining the ANN using online-training based on execution of the compute kernel. 
     
     
         19 . The computing device of  claim 11 , wherein substituting the ANN for the compute kernel comprises executing the ANN on the processor. 
     
     
         20 . The computing device of  claim 11 , wherein substituting the ANN for the compute kernel comprises executing the ANN on a different processor in communication with the processor.