Xilinx, Inc.

Chia-Jui Hsu

Vinod Kumar Kathail

Ralph D Wittig

Philip Bryn James-Roxby

Shail Aditya Gupta

Samuel R Bayliss

Mukund Sivaraman

Implementing data flows of an application across a memory hierarchy of a data processing array includes receiving a data flow graph specifying an application for execution on the data processing array. A plurality of buffer objects corresponding to a plurality of different levels of the memory hierarchy of the data processing array and an external memory are identified. The plurality of buffer objects specify data flows. Buffer object parameters are determined. The buffer object parameters define properties of the data flows. Data that configures the data processing array to implement the data flows among the plurality of different levels of the memory hierarchy and the external memory is generated based on the plurality of buffer objects and the buffer object parameters.

Implementing data flows of an application across a memory hierarchy of a data processing array

Modeling and compiling tensor processing applications using multi-layer adaptive data flow (ML-ADF) graphs, including folding the ML-ADF graph for temporal sharing of platform resources, computing schedules for runtime orchestration of kernel execution, memory reuse, tensor and sub-volume movement, and dataflow synchronization, and generating binary code for processors of the target computing platform and re-targetable controller code. The ML-ADF graph may represent: tensor processing of a layer of a neural network as data flow through the data nodes and distribution to compute tiles across memory hierarchy; data flow amongst layers of the neural network using connections amongst data nodes of the respective layers; and multi-dimension data partitioning and distribution using tiling parameters associated with ports of the data nodes.

Modeling and compiling tensor processing applications for a computing platform using multi-layer adaptive data flow graphs

Embodiments herein use control application programming interfaces (APIs) to control the execution of a dataflow graph in a heterogeneous processing system. That is, embodiments herein describe a programming model along with associated APIs and methods that can control, interact, and at least partially reconfigure a user application (e.g., the dataflow graph) executing on the heterogeneous processing system through a local executing control program. Using the control APIs, users can manipulate such remotely executing graphs directly as local objects and perform control operations on them (e.g., for loading and initializing the graphs; dynamically adjusting parameters for adaptive control; monitoring application parameters, system states and events; scheduling operations to read and write data across the distributed memory boundary of the platform; controlling the execution life-cycle of a subsystem; and partially reconfiguring the computing resources for a new subsystem).

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Santa Clara, CA, United States of America

Chia-Jui Hsu