Abstract: Systems and methods for firmware-based diagnostics in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, where the memory includes firmware instructions that, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and wherein at least one of the plurality of devices operates as an orchestrator configured to: execute or instruct a selected device among the plurality of devices to execute an Artificial Intelligence (AI) model configured to determine whether to trigger a diagnostics process; and, in response to the determination, trigger the diagnostics process.

Abstract: Systems and methods for firmware-based orchestration of Artificial Intelligence (AI) performance profiles in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory having a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: deploy or instruct another device among the plurality of devices to deploy an AI model configured to produce an inference based, at least in part, upon context or telemetry data received from a subset of the plurality of devices; and select an AI performance profile based, at least in part, upon the inference.

Abstract: Systems and methods for Artificial Intelligence (AI) model dependency handling in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory includes a plurality of sets of firmware instructions that, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: receive an indication of a relationship between a first AI model and a second AI model; and in response to an instruction to update the first AI model and in the absence of another instruction to update the second AI model, trigger installation of an update to the second AI model.

Abstract: Systems and methods for firmware-based Artificial Intelligence (AI) model authorization in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform having a plurality of devices and a memory including a plurality of sets of firmware instructions, where the firmware instructions, upon execution by a respective device among the plurality of devices, enable the respective device to provide a firmware service, and at least one of the devices operates as an orchestrator configured to: receive a request for deployment, loading, extraction, launch, or release of AI model data; and at least one of: in response to a successful authentication of the request, allow the deployment, extraction, launch, or release of the AI model data; or in response to a failed authentication of the request, deny the deployment, extraction, launch, or release of the AI model data.

Abstract: Systems and methods for firmware-based network management in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform and having firmware instructions that, upon execution, enables a respective device to provide a corresponding firmware service, where at least one of the devices operates as an orchestrator configured to: execute or instruct a selected device to execute an Artificial Intelligence (AI) model configured to identify or predict a network communication or performance issue and, in response to the determination, modify a configuration of a wireless controller based, at least in part, upon a policy received from an Information Technology Decision Maker (ITDM) or Original Equipment Manufacturer (OEM).

Abstract: Automatically updating a full charge capacity (FCC) of a battery of an information handling system, the method including: determining that the SOC of the battery is less than the SOC threshold; determining that the time since the previous update of the FCC of the battery is greater than the time threshold; identifying configuration parameters for an update of the FCC of the battery; comparing the configuration parameters with current conditions of the battery; based on comparing the configuration parameters with current conditions of the battery, determining that the current conditions of the battery are within bounds of the configuration parameters; and in response to determining that the current conditions of the battery are within the bounds of the configuration parameters, updating the FCC of the battery.

Abstract: Systems and methods for managing consumption of alternating-current (AC) power by an Information Handling System (IHS). In a non-limiting embodiment, an IHS may include: a heterogeneous computing platform comprising a plurality of devices and a memory, the memory having a plurality of sets of firmware instructions, where each of the sets, upon execution by a respective device, enables that device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: execute or instruct one or more devices to execute one or more Artificial Intelligence models usable to characterize usage profiles based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices; and, based upon the usage profiles, determine whether to allow a battery to power the IHS at least partially while the IHS is coupled to an AC power source.

Abstract: Systems and methods for battery management in heterogenous computing platforms. In a non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, where the memory includes a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices; and execute or instruct one or more selected devices among the plurality of devices to execute one or more Artificial Intelligence models usable to manage a charging or discharging operation of a battery based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for enabling contextual privacy controls in heterogenous computing platforms are described. In a non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, the memory including a plurality of sets of firmware instructions that, upon execution by a respective device among the plurality of devices, enable the respective device to provide a corresponding firmware service, where at least one of the plurality of devices operates as an orchestrator configured to: execute or instruct a device to execute an Artificial Intelligence model usable to identify one of a plurality of privacy modes to be applied to the IHS based, at least in part, upon context data received from at least a subset of the plurality of devices; and, based upon the identified privacy mode, modify one or more IHS settings.

Abstract: Systems and methods for firmware-based switching between user profiles in a heterogenous computing platform. In an illustrative, non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform comprising a plurality of devices, and a memory coupled to the platform having a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a firmware service, and at least one of the devices operates as an orchestrator configured to: execute or instruct a device to execute an Artificial Intelligence model configured to produce an inference result based, at least in part, upon context or telemetry data received from a subset of the plurality of devices; select one of a plurality of user profiles based upon the inference result; and apply a setting associated with the selected user profile to a selected device.

Abstract: Systems and methods for enabling localized image enhancements in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform having a plurality of devices and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from a subset of one or more of the plurality of devices; and instruct a selected device among the plurality of devices to execute an Artificial Intelligence model configured to produce a localized image based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for detecting video gaming sessions in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the heterogeneous computing platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct a device among the plurality of devices to execute an Artificial Intelligence (AI) model configured to detect a video gaming session based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for firmware-based user persona discovery in a heterogenous computing platform. In a non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform having a plurality of devices, and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct one or more selected devices to execute one or more Artificial Intelligence models configured to discover a user persona associated with a user of the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for managing embedded Subscriber Identity Module (eSIM) credentials in heterogenous computing platforms. In a non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, where the memory includes firmware instructions that, upon execution by a respective device among the plurality of devices, enable a respective device to provide a corresponding firmware service, and at least one of the devices operates as an orchestrator configured to: deploy or instruct another device to deploy an AI model configured to produce an inference result based, at least in part, upon context or telemetry data received from a subset of the devices; select an eSIM profile based upon the inference result; and transmit a credential within the selected eSIM profile to a wireless controller configured to authenticate the credential.

Abstract: Systems and methods for enabling collaboration session migration in heterogenous computing platforms. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory coupled to the platform, the memory having a plurality of sets of firmware instructions that, upon execution by a respective device among the plurality of devices, enable the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: execute or instruct a device selected among the plurality of devices to execute an AI model usable to identify a migration opportunity for an ongoing collaboration session based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, and notify another IHS to handle at least a portion of the ongoing collaboration session.

Abstract: Systems and methods for voice-assisted wireless docking in heterogenous computing platforms are described. In an illustrative, non-limiting embodiment, an Information Handling System may include a heterogeneous computing platform having a plurality of devices, and a memory coupled to the heterogeneous computing platform, where the memory includes a plurality of sets of firmware instructions, each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and at least one of the plurality of devices operates as an orchestrator configured to: detect a predetermined utterance or direct a first one of the plurality of devices to detect the predetermined utterance; and, in response to the detection, direct a second one of the plurality of devices to: (a) connect the IHS to a wireless dock, or (b) disconnect the IHS from the wireless dock.

Abstract: Systems and methods for deploying instances of Artificial Intelligence (AI) models having different levels of complexity in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS), may include a heterogeneous computing platform comprising a plurality of devices and a memory having a plurality of sets of firmware instructions that, upon execution by a respective device, enable the respective device to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: select an instance of an AI model among a plurality of instances of the AI model based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, where each of the plurality of instances of the AI model has a different level of complexity, and instruct a device to execute the selected instance of the AI model.

Abstract: Systems and methods for usage detection and notification in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices and instruct one or more selected devices to execute one or more Artificial Intelligence (AI) models configured to characterize one or more workloads executed by the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for runtime control of Artificial Intelligence (AI) model parameters in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct a device among the plurality of devices to modify a parameter of an AI model executed by the device based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for Artificial Intelligence (AI) model characterization and offloading in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables it to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: receive a characterization result from a first device, where the first device is configured to execute a first AI model configured to characterize a second AI model executed by a second device, and, prior to a subsequent execution of the second AI model, assign the second AI model to a third device based, at least in part, upon the characterization result.