Neural-Network Logic-Gate Mapping for Adaptive Circuit Configuration
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Solution Overview
Problem
Existing information handling systems lack efficient methods for configuring integrated circuits based on neural networks, limiting their adaptability and performance in processing and storing information.
Innovation Solution
A process that determines a neural network from input and output data, converts it into binary neural networks, and configures integrated circuits such as FPGAs, ASICs, and CPLDs using logic gates, enabling efficient configuration through machine learning and hardware description languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used to configure integrated circuits, then the configuration process is simple and straightforward, but the adaptability and performance of the system are limited
Solution Approach 1:
The patent replaces traditional manual or script-based configuration methods with an automated machine learning-based system. The machine learning model automatically determines neural network architectures and generates configuration instructions for integrated circuits, substituting mechanical programming approaches with intelligent automation to improve adaptability while managing complexity through automated decision-making
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the desired system performance and the actual circuit configuration. This intermediary component analyzes performance requirements, determines optimal neural network architectures, and generates configuration instructions, thereby bridging the gap between high-level performance goals and low-level hardware configuration without requiring direct manual intervention
2Productivity
If manual configuration methods are used, then the control over the configuration process is high, but the speed and efficiency of configuration is low
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on various configuration scenarios and performance requirements. The model automatically determines neural network architectures and generates configuration instructions before the actual circuit fabrication or deployment, enabling rapid configuration without requiring manual analysis and design during the execution phase
Solution Approach 2:
The system performs self-service configuration where the machine learning model autonomously analyzes performance requirements, determines optimal architectures, generates configuration instructions, and validates the configuration without requiring manual intervention. This self-service approach dramatically increases configuration speed while the model learns from historical data to maintain high accuracy
3Use of energy by moving object
If conventional configuration approaches are used, then the implementation is straightforward, but the energy consumption and performance optimization are insufficient
Solution Approach 1:
The patent applies parameter changes by optimizing various configuration parameters such as neural network architecture, layer depths, activation functions, and hardware resource allocation. The machine learning model automatically adjusts these parameters to achieve optimal performance while minimizing energy consumption, transforming the configuration process from fixed templates to adaptive parameter optimization
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model evaluates configuration outcomes and uses this feedback to refine future configurations. Performance metrics and energy consumption data are fed back into the model to continuously improve configuration quality, enabling iterative optimization that balances performance and energy efficiency while maintaining ease of implementation through automated feedback loops
Data Source
AI summary
In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may execute a process; provide input data to the process as the process executes; receive output data from the process as the process executes; after executing the process, determine a neural network based at least on the input data to the process and the output data from the process; determine multiple binary neural networks from the neural network; determine a network of multiple logic gates based at least on the multiple binary neural networks of the neural network; and configure an integrated circuit based at least on the network of the multiple logic gates. For example, the integrated circuit may include at least one of a field programmable gate array, an application specific integrated circuit, a programmable array logic, and a complex logic device.