Processor-on-Demand Microgrid With AI Energy Routing and Peer Networking
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Solution Overview
Problem
Current power management systems in high-performance computing are inefficient, leading to high operational costs, environmental impact, and limited accessibility to GPUs due to geographical centralization, which results in increased latencies and vulnerability to disruptions.
Innovation Solution
A bi-directional electrical microgrid of processor-on-demand systems with integrated renewable energy sources, advanced AI for energy management, and secure peer-to-peer networking, enabling scalable and user-friendly access to computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If GPUs are housed in large centralized data centers, then computational power is consolidated and managed centrally, but energy consumption increases and environmental impact worsens
Solution Approach 1:
The patent segments the centralized data center into distributed edge computing nodes deployed across multiple locations. Each edge device contains GPU capabilities that can be independently utilized, transforming the monolithic centralized structure into a distributed network of smaller computational units that collectively provide the same computational power while reducing energy concentration and transmission losses.
Solution Approach 2:
The patent introduces a new dimensional approach by deploying computing resources across geographical space rather than concentrating them in a single location. Edge devices are distributed throughout the network infrastructure, adding a spatial dimension to resource allocation that reduces energy transmission distances and enables localized energy management.
2Ease of manufacture
If data centers are statically configured, then infrastructure costs are predictable, but resource utilization efficiency decreases due to overprovisioning and underutilization
Solution Approach 1:
The patent implements dynamic resource allocation where edge computing resources can be flexibly activated and deactivated based on real-time demand. The system transitions from static infrastructure to dynamic resource orchestration, allowing computational resources to be provisioned on-demand across the distributed network, improving utilization efficiency while maintaining infrastructure stability through controlled activation patterns.
Solution Approach 2:
The patent creates multi-functional edge computing nodes that can serve multiple purposes: local data processing, cloud gateway functions, and collaborative computing resources. Each edge device is designed to perform various computational tasks and adapt its functionality based on workload requirements, eliminating the need for dedicated specialized infrastructure for each function.
3Device complexity
If computing resources are geographically centralized, then infrastructure management is simplified, but accessibility for remote users decreases leading to increased latencies
Solution Approach 1:
The patent segments the centralized computing resource into distributed edge nodes positioned geographically closer to end users. This segmentation transforms the single remote data center into multiple local computing points, reducing the physical distance data must travel and thereby decreasing latency for remote users while maintaining manageable infrastructure through standardized node designs.
Solution Approach 2:
The patent introduces edge devices as intermediary computing nodes between end users and the core data center. These intermediaries handle local data processing and preprocessing tasks, reducing the need for constant communication with the centralized data center and thereby improving access speed while maintaining simplified centralized management for core functions.
4Extent of automation
If centralized data centers are used, then resource allocation is centrally controlled, but vulnerability to localized power outages and network disruptions increases
Solution Approach 1:
The patent segments the monolithic centralized system into distributed independent edge computing nodes. Each node operates autonomously with local resource allocation capabilities, eliminating the single point of failure inherent in centralized systems. This segmentation ensures that localized disruptions affect only individual nodes rather than the entire system, thereby improving reliability while maintaining automated resource allocation through distributed coordination protocols.
Solution Approach 2:
The patent changes the operational parameters of resource allocation from centralized control to distributed autonomous decision-making. Each edge node adjusts its local resource allocation based on real-time conditions and communicates with peers to maintain system-wide optimization, transforming the control parameter from top-down centralized management to bottom-up distributed coordination, thereby improving system reliability.
Data Source
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AI summary
A smart, bi-directional electrical microgrid includes processor-on-demand systems, including a computing device having a processing unit and a memory, an Energy Management System (EMS) configured for regulating power usage and optimizing energy efficiency, a distributed power resource for providing a stable and efficient energy supply, a database configured to store energy metrics, a Large Language Model (LLM) for processing the energy metrics stored to generate an energy management plan, an API gateway providing external systems secure, on-demand access to the processing unit, a software module for managing the processor-on-demand system according to the energy management plan and a communication module for peer-to-peer networking, routing, and forwarding. The microgrid also includes one or more management servers for managing the delivery and distribution of power among the processor-on-demand systems to optimize efficiency and uptime, and a network of power lines that interconnect the processor-on-demand systems.