Firmware AI Profile Orchestration for OS-Independent Heterogeneous Platforms
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Solution Overview
Problem
The transition from x86 to ARM-based processors in Information Handling Systems (IHSs) presents challenges in management, customization, optimization, and configuration, particularly in managing Artificial Intelligence (AI) performance profiles without involving the host Operating System (OS).
Innovation Solution
A heterogeneous computing platform with a plurality of devices, each executing firmware instructions, is orchestrated by an orchestrator to deploy AI models and select performance profiles based on context or telemetry data, using APIs to manage AI model deployment and switching without OS involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI performance profiles are managed through host Operating System, then system compatibility and ease of operation are improved, but system complexity and management overhead increase
Solution Approach 1:
The patent extracts AI performance profile management from the host Operating System by implementing a dedicated firmware-based orchestrator. This orchestrator independently manages AI models, performance profiles, and device resource allocation without requiring OS involvement, thereby reducing system complexity while maintaining ease of operation through automated firmware-level control
Solution Approach 2:
The patent introduces a firmware-based orchestrator as an intermediary layer between AI applications and hardware devices. This orchestrator mediates resource allocation, model deployment, and performance optimization tasks, eliminating the need for direct OS involvement and reducing overall system complexity while preserving operational simplicity
2Productivity
If AI models are deployed with high performance requirements, then computational capability and productivity are improved, but power consumption and energy usage increase
Solution Approach 1:
The patent implements dynamic performance profiling that allows AI models to operate at different performance levels based on real-time system conditions. The firmware orchestrator dynamically adjusts model complexity, precision, and computational resources allocated to each AI task, enabling high productivity when needed while reducing power consumption during lower-demand periods
Solution Approach 2:
The patent changes key performance parameters such as computational precision, model complexity, and resource allocation thresholds to optimize the balance between productivity and power consumption. By adjusting these parameters at the firmware level, the system achieves high performance when necessary while minimizing energy usage during normal operation
3Adaptability or versatility
If multiple AI models are maintained for different performance levels, then adaptability and versatility are improved, but device complexity and management overhead increase
Solution Approach 1:
The patent creates a universal firmware-based orchestrator that manages multiple AI models with different performance characteristics through a single integrated framework. This orchestrator provides unified functionality for model deployment, resource allocation, and performance monitoring across all AI workloads, thereby achieving high adaptability while avoiding the complexity that would arise from separate management systems for each model type
Solution Approach 2:
The patent segments AI model management into distinct firmware modules that handle different performance profiles and device resources independently. This modular segmentation allows the system to maintain multiple AI models with different characteristics while organizing them through a structured firmware architecture that reduces overall management complexity through clear separation of concerns
Data Source
AI summary
Orchestration of Artificial Intelligence (AI) performance profiles and AI models, based upon context or telemetry data, performed by an orchestrator of a heterogenous computing platforms is 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.