# US 2026/0074969 A1 · Apparatus, articles of manufacture, and methods to partition neural networks for execution at distributed edge nodes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0074969 A1 - Document ID: US20260074969A1 - Status: Published application - Kind: A1 - Application number: 19/349,698 - Filed: 2025-10-03 - Published: 2026-03-12 - Earliest claimed: 2021-12-17 - Assignees: Intel Corporation - Applicants: Intel Corporation - Inventors: Karthik Kumar; Francesc Guim Bernat - Web page: https://patentgrep.com/patent/US20260074969A1 ## CPC - H04L 41/5054: Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components - G06F 9/5094: {where the allocation takes into account power or heat criteria (power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206)} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - H04L 41/16: using machine learning or artificial intelligence - H04L 41/5019: Ensuring fulfilment of SLA - H04L 67/34: {involving the movement of software or configuration parameters (network booting or remote initial program loading [RIPL] G06F9/4416)} - G06N 3/08: Learning methods - H04L 41/0833: for reduction of network energy consumption - H04L 43/08: Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters - H04L 43/0894: Packet rate - H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Methods, apparatus, systems, and articles of manufacture are disclosed to partition neural network models for executing at distributed Edge nodes. An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions. One or more of the at least one processor circuit is to partition a neural network model into a first portion to be executed at an edge of a network and a second portion to be executed at a cloud based on a transmission metric. ## Claims ### Claim 1 An apparatus comprising: interface circuitry; machine-readable instructions; and at least one processor circuit to be programmed by the machine-readable instructions to partition a neural network model into a first portion to be executed at an edge of a network and a second portion to be executed at a cloud based on a transmission metric. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the transmission metric includes a first estimated amount of time to transmit an intermediate result of executing the first portion of the neural network model from the edge to the cloud and a second estimated amount of time to transmit a final result of executing the second portion of the neural network model from the cloud to the edge. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein one or more of the at least one processor circuit is to determine the transmission metric based on an available transmission bandwidth of the edge and a data size of (1) an intermediate result of executing the first portion of the neural network model and (2) a final result of executing the second portion of the neural network model. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the transmission metric includes an estimated amount of energy that would be consumed by transmission of an intermediate result of executing the first portion of the neural network model from the edge to the cloud. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein one or more of the at least one processor circuit is to determine the transmission metric based on an ambient condition at the edge and a power supply associated with the edge. ### Claim 6 (depends on 5) The apparatus of claim 5, wherein the ambient condition includes at least one of temperature, wind speed, or humidity. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein one or more of the at least one processor circuit is to associate a final result from the cloud with a request to execute the neural network model, the final result based on executing the second portion of the neural network model on an intermediate result of executing the first portion of the neural network model. ### Claim 8 A non-transitory computer-readable medium comprising instructions to cause at least one processor circuit to partition a neural network model into a first portion to be executed at an edge of a network and a second portion to be executed at a cloud based on a transmission metric. ### Claim 9 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the transmission metric includes a first estimated amount of time to transmit an intermediate result of executing the first portion of the neural network model from the edge to the cloud and a second estimated amount of time to transmit a final result of executing the second portion of the neural network model from the cloud to the edge. ### Claim 10 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instructions cause one or more of the at least one processor circuit to determine the transmission metric based on an available transmission bandwidth of the edge and a data size of (1) an intermediate result of executing the first portion of the neural network model and (2) a final result of executing the second portion of the neural network model. ### Claim 11 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the transmission metric includes an estimated amount of energy that would be consumed by transmission of an intermediate result of executing the first portion of the neural network model from the edge to the cloud. ### Claim 12 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instructions cause one or more of the at least one processor circuit to determine the transmission metric based on an ambient condition at the edge and a power supply associated with the edge. ### Claim 13 (depends on 12) The non-transitory computer-readable medium of claim 12, wherein the ambient condition includes at least one of temperature, wind speed, or humidity. ### Claim 14 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the instructions cause one or more of the at least one processor circuit to associate a final result from the cloud with a request to execute the neural network model, the final result based on executing the second portion of the neural network model on an intermediate result of executing the first portion of the neural network model. ### Claim 15 An apparatus comprising: means for sending and receiving; and means for partitioning a neural network model into a first portion to be executed at an edge of a network and a second portion to be executed at a cloud based on a transmission metric. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the transmission metric includes a first transmission time to transmit an intermediate result of executing the first portion of the neural network model from the edge to the cloud and a second transmission time to transmit a final result of executing the second portion of the neural network model from the cloud to the edge. ### Claim 17 (depends on 15) The apparatus of claim 15, wherein the transmission metric includes a transmission time, and the apparatus includes means for calculating the transmission time based on an available transmission bandwidth of the edge and a data size of (1) an intermediate result of executing the first portion of the neural network model and (2) a final result of executing the second portion of the neural network model. ### Claim 18 (depends on 15) The apparatus of claim 15, wherein the transmission metric includes a transmission energy consumption for transmitting an intermediate result of executing the first portion of the neural network model from the edge to the cloud. ### Claim 19 (depends on 15) The apparatus of claim 15, wherein the transmission metric includes a transmission energy consumption, and the apparatus includes means for determining the transmission energy consumption based on an ambient condition at the edge and a power supply associated with the edge. ### Claim 20 (depends on 15) The apparatus of claim 15, wherein the means for sending and receiving is to receive a final result from the cloud, the final result based on executing the second portion of the neural network model on an intermediate result of executing the first portion of the neural network model.