DEEP LEARNING ACCELERATOR SYSTEM INTERFACE

Systems are methods are provided for implementing a deep learning accelerator system interface (DLASI). The DLASI connects an accelerator having a plurality of inference computation units to a memory of the host computer system during an inference operation. The DLASI allows interoperability between...

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Bibliographic Details
Main Authors Lee, Eun Sub, Brueggen, Chris Michael, WARNER, CRAIG
Format Patent
LanguageEnglish
Published 15.04.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Systems are methods are provided for implementing a deep learning accelerator system interface (DLASI). The DLASI connects an accelerator having a plurality of inference computation units to a memory of the host computer system during an inference operation. The DLASI allows interoperability between a main memory of a host computer, which uses 64 B cache lines, for example, and inference computation units, such as tiles, which are designed with smaller on-die memory using 16-bit words. The DLASI can include several components that function collectively to provide the interface between the server memory and a plurality of tiles. For example, the DLASI can include: a switch connected to the plurality of tiles; a host interface; a bridge connected to the switch and the host interface; and a deep learning accelerator fabric protocol. The fabric protocol can also implement a pipelining scheme which optimizes throughput of the multiple tiles of the accelerator.
Bibliography:Application Number: US201916598329