# US 2025/0148769 A1 · Efficient execution of machine learning models using partitioning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0148769 A1 - Document ID: US20250148769A1 - Status: Published application - Kind: A1 - Application number: 18/504,889 - Filed: 2023-11-08 - Published: 2025-05-08 - Earliest claimed: 2023-11-08 - Assignees: QUALCOMM Incorporated - Applicants: QUALCOMM Incorporated - Inventors: Alexander BEYKUN; Sahil GUPTA; Jian WANG; Prathamesh Prakash PRABHUDESAI; Lin WANG; Nathan William LEE; Veluppillai ARULESAN; Jeffrey Baginsky GEHLHAAR - Web page: https://patentgrep.com/patent/US20250148769A1 ## CPC - G06N 3/098: Distributed learning, e.g. federated learning - G06V 10/82: using neural networks - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/0475: Generative networks - G06N 3/063: using electronic means - G06N 3/08: Learning methods - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Certain aspects provide techniques and apparatuses for efficient operation of a machine learning model based on partitioning the machine learning model. An example method generally includes receiving a graph for a machine learning model. The graph for the machine learning model generally includes a plurality of subgraphs representing different portions of the machine learning model. The machine learning model is instantiated across a plurality of process domains associated with a same application based on the plurality of subgraphs in the graph for the machine learning model. An inference is generated based on executing the machine learning model across the plurality of process domains, and one or more actions are taken based on the generated inference. ## Claims ### Claim 1 A processor-implemented method, comprising: receiving a graph for a machine learning model, the graph for the machine learning model including a plurality of subgraphs representing different portions of the machine learning model; instantiating the machine learning model across a plurality of process domains associated with a same application based on the plurality of subgraphs in the graph for the machine learning model; generating an inference based on executing the machine learning model across the plurality of process domains; and taking one or more actions based on the generated inference. ### Claim 2 (depends on 1) The method of claim 1, wherein instantiating the machine learning model across the plurality of process domains comprises instantiating a portion of the machine learning model represented by a corresponding subgraph from the plurality of subgraphs based on an amount of memory associated with the portion of the machine learning model and an available amount of memory on a process domain from the plurality of process domains. ### Claim 3 (depends on 1) The method of claim 1, wherein instantiating the machine learning model across the plurality of process domains comprises mapping outputs of a first subgraph from the plurality of subgraphs to inputs of a second subgraph of the plurality of subgraphs. ### Claim 4 (depends on 1) The method of claim 1, wherein each respective subgraph of the plurality of subgraphs is associated with a respective memory space shared within the same application. ### Claim 5 (depends on 1) The method of claim 1, wherein executing the machine learning model across the plurality of process domains comprises sequentially executing the plurality of subgraphs based on outputs of a first subgraph in the plurality of subgraphs corresponding to inputs of a second subgraph in the plurality of subgraphs. ### Claim 6 (depends on 5) The method of claim 5, wherein sequentially executing the plurality of subgraphs comprises an atomic operation. ### Claim 7 (depends on 1) The method of claim 1, further comprising releasing the plurality of process domains to terminate execution of the machine learning model. ### Claim 8 (depends on 1) The method of claim 1, wherein the machine learning model comprises a generative artificial intelligence model. ### Claim 9 (depends on 8) The method of claim 8, wherein the one or more actions comprise generating a response to an input query using the generative artificial intelligence model. ### Claim 10 (depends on 1) The method of claim 1, wherein the machine learning model comprises a classifier neural network. ### Claim 11 (depends on 10) The method of claim 10, wherein the one or more actions comprise generating one or more control signals to control an autonomous vehicle based on a classification of one or more objects in a scene generated by the classifier neural network. ### Claim 12 (depends on 10) The method of claim 10, wherein the one or more actions comprise applying different levels of compression to different portions of an image based on classifications of different objects in the image generated by the classifier neural network. ### Claim 13 A processing system, comprising: a memory having executable instructions stored thereon; and one or more processors coupled to the memory and configured to execute the executable instructions in order to cause the processing system to: receive a graph for a machine learning model, the graph for the machine learning model including a plurality of subgraphs representing different portions of the machine learning model; instantiate the machine learning model across a plurality of process domains associated with a same application based on the plurality of subgraphs in the graph for the machine learning model; generate an inference based on executing the machine learning model across the plurality of process domains; and take one or more actions based on the generated inference. ### Claim 14 (depends on 13) The processing system of claim 13, wherein to instantiate the machine learning model across the plurality of process domains, the one or more processors are configured to cause the processing system to instantiate a portion of the machine learning model represented by a corresponding subgraph from the plurality of subgraphs based on an amount of memory associated with the portion of the machine learning model and an available amount of memory on a process domain from the plurality of process domains. ### Claim 15 (depends on 13) The processing system of claim 13, wherein to instantiate the machine learning model across the plurality of process domains, the one or more processors are configured to cause the processing system to map outputs of a first subgraph from the plurality of subgraphs to inputs of a second subgraph of the plurality of subgraphs. ### Claim 16 (depends on 13) The processing system of claim 13, wherein each respective subgraph of the plurality of subgraphs is associated with a respective memory space shared within the same application. ### Claim 17 (depends on 13) The processing system of claim 13, wherein to execute the machine learning model across the plurality of process domains, the one or more processors are configured to cause the processing system to sequentially execute the plurality of subgraphs based on outputs of a first subgraph in the plurality of subgraphs corresponding to inputs of a second subgraph in the plurality of subgraphs. ### Claim 18 (depends on 17) The processing system of claim 17, wherein sequentially executing the plurality of subgraphs comprises an atomic operation. ### Claim 19 (depends on 13) The processing system of claim 13, wherein the one or more processors are further configured to cause the processing system to release the plurality of process domains to terminate execution of the machine learning model. ### Claim 20 (depends on 13) The processing system of claim 13, wherein the machine learning model comprises a generative artificial intelligence model. ### Claim 21 (depends on 20) The processing system of claim 20, wherein the one or more actions comprise generating a response to an input query using the generative artificial intelligence model. ### Claim 22 (depends on 13) The processing system of claim 13, wherein the machine learning model comprises a classifier neural network. ### Claim 23 (depends on 22) The processing system of claim 22, wherein the one or more actions comprise generating one or more control signals to control an autonomous vehicle based on a classification of one or more objects in a scene generated by the classifier neural network. ### Claim 24 (depends on 22) The processing system of claim 22, wherein the one or more actions comprise applying different levels of compression to different portions of an image based on classifications of different objects in the image generated by the classifier neural network. ### Claim 25 A processing system, comprising: means for receiving a graph for a machine learning model, the graph for the machine learning model including a plurality of subgraphs representing different portions of the machine learning model; means for instantiating the machine learning model across a plurality of process domains associated with a same application based on the plurality of subgraphs in the graph for the machine learning model; means for generating an inference based on executing the machine learning model across the plurality of process domains; and means for taking one or more actions based on the generated inference. ### Claim 26 (depends on 25) The processing system of claim 25, wherein the means for instantiating the machine learning model across the plurality of process domains comprises means for instantiating a portion of the machine learning model represented by a corresponding subgraph from the plurality of subgraphs based on an amount of memory associated with the portion of the machine learning model and an available amount of memory on a process domain from the plurality of process domains. ### Claim 27 (depends on 25) The processing system of claim 25, wherein the means for instantiating the machine learning model across the plurality of process domains comprises means for mapping outputs of a first subgraph from the plurality of subgraphs to inputs of a second subgraph of the plurality of subgraphs. ### Claim 28 (depends on 25) The processing system of claim 25, wherein the means for executing the machine learning model across the plurality of process domains comprises means for sequentially executing the plurality of subgraphs based on outputs of a first subgraph in the plurality of subgraphs corresponding to inputs of a second subgraph in the plurality of subgraphs. ### Claim 29 (depends on 25) The processing system of claim 25, further comprising means for releasing the plurality of process domains to terminate execution of the machine learning model. ### Claim 30 A non-transitory computer-readable medium having executable instructions stored thereon which, when executed by one or more processors, perform an operation comprising: receiving a graph for a machine learning model, the graph for the machine learning model including a plurality of subgraphs representing different portions of the machine learning model; instantiating the machine learning model across a plurality of process domains associated with a same application based on the plurality of subgraphs in the graph for the machine learning model; generating an inference based on executing the machine learning model across the plurality of process domains; and taking one or more actions based on the generated inference.