T-head (shanghai) Semiconductor Co., Ltd.

Chengyuan Wu

Liang Han

Rong Zhong

Guoyu Zhu

YunXiao Zou

Yang Jiao

Ye Lu

The presented systems enable efficient and effective network communications. In one embodiment, a system comprises a parallel processing unit (PPU) included in a chip and a plurality of interconnects in an inter-chip network (ICN) configured to communicatively couple a plurality of PPUs that communicate with one another via the ICN. Corresponding communications are configured in accordance with routing tables. The routing tables can be stored and reside in registers of an ICN subsystem included in the PPU and include indications of minimum links available to forward a communication from the PPU and another PPU. Respective ones of the routing tables include indications of a correlation between the minimum links and respective ones of a plurality of egress ports that are available for communication coupling to the respective ones of PPUs that are possible destination PPUs. The routing tables can include indications of a single path between two PPUs per respective information communication flow.

Parallel dataflow routing scheme systems and methods

Systems and methods for exchanging synchronization information between processing units using a synchronization network are disclosed. The disclosed systems and methods include a device including a host and associated neural processing units. Each of the neural processing units can include a command communication module and a synchronization communication module. The command communication module can include circuitry for communicating with the host device over a host network. The synchronization communication module can include circuitry enabling communication between neural processing units over a synchronization network. The neural processing units can be configured to each obtain a synchronized update for a machine learning model. This synchronized update can be obtained at least in part by exchanging synchronization information using the synchronization network. The neural processing units can each maintain a version of the machine learning model and can synchronize it using the synchronized update.

Neural processing unit synchronization systems and methods

Virtualization techniques can include determining virtual function routing tables for the virtual parallel processing units (PPUs) from a logical topology of a virtual function. A first mapping of the virtual PPUs to a first set of a plurality of physical PPUs can be generated. Virtualization can also include generating a first set of physical function routing tables for the first set of physical PPUs based on the virtual function tables and the first virtual PPU to physical PPU mapping. An application can be migrated from the first set of physical PPUs to a second set of PPUs by generating a second mapping of the virtual PPUs to a second set of a plurality of physical PPUs. A second set of physical function routing table for the second set of physical PPUs can also be generated based on the virtual function tables and the second virtual PPU to physical PPU mapping.

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Fremont, CA, United States of America

Chengyuan Wu