Deep neural networks compiler for a trace-based accelerator
Abstract:
A method of compiling neural network code to executable instructions for execution by a computational acceleration system having a memory circuit and one or more acceleration circuits having a maps data buffer and a kernel data buffer is disclosed, such as for execution by an inference engine circuit architecture which includes a matrix-matrix (MM) accelerator circuit having multiple operating modes to provide a complete matrix multiplication. A representative compiling method includes generating a list of neural network layer model objects; fusing available functions and layers in the list; selecting a cooperative mode, an independent mode, or a combined cooperative and independent mode for execution; selecting a data movement mode and an ordering of computations which reduces usage of the memory circuit; generating an ordered sequence of load objects, compute objects, and store objects; and converting the ordered sequence of load objects, compute objects, and store objects into the executable instructions.
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