Invention Grant
- Patent Title: Deep neural networks compiler for a trace-based accelerator
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Application No.: US17003476Application Date: 2020-08-26
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Publication No.: US11861337B2Publication Date: 2024-01-02
- Inventor: Andre Xian Ming Chang , Aliasger Zaidy , Eugenio Culurciello , Marko Vitez
- Applicant: Micron Technology, Inc.
- Applicant Address: US ID Boise
- Assignee: Micron Technology, Inc.
- Current Assignee: Micron Technology, Inc.
- Current Assignee Address: US ID Boise
- Agency: Gamburd Law Group LLC
- Agent Nancy R. Gamburd
- Main IPC: G06F8/41
- IPC: G06F8/41 ; G06F9/30 ; G06F9/50 ; G06N3/02

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.
Public/Granted literature
- US20220066760A1 Deep Neural Networks Compiler for a Trace-Based Accelerator Public/Granted day:2022-03-03
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