Abstract: A processor architecture and model exploration system for deep learning is provided. A method of improving performance of a processor system and associated software includes selecting a set of performance parameter targets for a processor architecture having a set of functional units and an AI model. The method also includes evaluating performance of the processor architecture and the AI model and adjusting at least one of the functional units of the processor architecture to form a new processor architecture prior to iteratively evaluating the combination of the new processor architecture and the AI model. Further, the method includes repeating the evaluating step and the adjustment step until the performance evaluation of the processor architecture and AI model meets the set of performance parameter targets.

Abstract: A system and method of generating an efficient neural network model architecture and an efficient processor for deep learning in an artificial intelligence (AI) processor are provided. The system and method to create the processor architecture as a companion to the neural network model by composing a plurality of processor architectures to enable architectural exploration. The compilation can be implemented for any arbitrary spatial processor architecture using either ASIC or FPGA devices. The processor architecture can be uniquely defined for a selected ML or AI model without having to update the software compiler.