Marvell Asia Pte., Ltd.

Pranav Jonnalagadda

Ulf Hanebutte

Senad Durakovic

Chien-Chun Chou

Nikhil Bernard John Stephen

A method includes receiving a machine learning (ML) network model in high-level code; generating an internal representation (IR), the IR is mapped to components in a multi-processing tile device; determining whether a first processing tile with a first on-chip memory (OCM) has a same dimension for an input/output tensor data as a second processing tile with a second OCM performing a same primitive function based on the IR; allocating a same memory address range within the first and the second OCM for the same primitive function if the first processing tile has the same dimension for the input/output tensor data as the second processing tile for the same primitive function; linking the memory address range of the first OCM to the memory address range of the second OCM to form a grouped memory space within the first and the second OCM respectively; and compiling low-level instructions based on the linking.

Method and system for memory management within machine learning inference engine

A system and method for splitting a machine learning (ML) graph is disclosed. The system includes a compiler configured to receive an ML model. The compiler generates a graph associated with the ML model, wherein the graph is an internal representation of the ML model. The graph is partitioned into a first subgraph and a second subgraph. The first subgraph is associated with an ML hardware, an ML emulator, or a combination thereof, and the second subgraph is associated with a processor different from the ML hardware. A set of low-level instructions associated with the first subgraph is generated. One or more resources in the ML hardware is identified to execute the set of low-level instructions associated with the first subgraph.

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San Jose, CA, United States of America

Pranav Jonnalagadda