Abstract: A method of constructing sub-graphs, includes receiving a directed acyclic graph (DAG), partitioning the directed acyclic graph into an at least one section, determining at least one hardware attribute, determining at least one DAG hardware limitation of the at least one section and determining a largest continuous node list of the at least one section in which the at least one hardware attribute meets the at least one DAG hardware limitation.

Abstract: A method of quantization evaluation, including, receiving a floating point data set, determining a floating point neural network model output utilizing the floating point data set, quantizing the floating point data set utilizing a quantization model yielding a quantized data set, determining a quantized neural network model output utilizing the quantized data set, determining whether an accuracy error between the floating point neural network model output and the quantized neural network model output exceeds an predetermined error tolerance, determining a floating point neural network tensor output utilizing the floating point data set if the predetermined error tolerance is exceeded, determining a quantized neural network tensor output utilizing the quantized data set if the predetermined error tolerance is exceeded, determining a per-tensor error based on the floating point neural network tensor output and the quantized neural network tensor output and updating the quantization model based on the per-tenso