Neural Network Configuration via SMT Solver
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Solution Overview
Problem
Implementing and configuring neural networks on diverse platforms is challenging due to hardware and software constraints, requiring significant time and resources to explore valid configurations that satisfy performance and resource metrics, often resulting in invalid configurations.
Innovation Solution
A system and method using satisfiability solvers, such as SMT solvers, to determine valid neural network configurations by identifying decision points, constraints, and performance metrics, generating candidate configurations that satisfy platform-specific constraints and performance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration exploration is performed to satisfy platform constraints, then configuration validity can be ensured, but significant time and resources are consumed
Solution Approach 1:
The patent replaces manual mechanical exploration of configuration spaces with an automated computer-based system that uses constraint satisfaction algorithms and search algorithms to automatically generate and validate neural network configurations, eliminating the need for manual consultation of documentation and manual configuration exploration
Solution Approach 2:
The patent introduces an intermediary configuration system that acts as a mediator between the neural network model and the hardware platform, automatically translating model requirements into platform-specific configurations while satisfying all constraints, thereby eliminating direct manual intervention
2Productivity
If comprehensive configuration exploration is performed to find optimal settings, then performance metrics can be optimized, but resource consumption increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-defining all possible constraints and decision points before the configuration search begins, allowing the system to efficiently navigate the configuration space using constraint satisfaction algorithms without exhaustive exploration, thereby reducing computational resources while still finding optimal configurations
Solution Approach 2:
The patent segments the configuration problem into discrete decision points and constraints, allowing the system to process and evaluate configurations in a structured, modular manner that reduces overall computational complexity and resource consumption
3Adaptability or versatility
If multiple decision points are considered for configuration, then configuration completeness improves, but the number of potential configurations increases drastically
Solution Approach 1:
The patent applies dynamics by making the configuration system adaptive and flexible, allowing it to dynamically adjust the search strategy based on constraints and performance metrics, thereby handling multiple decision points efficiently without being overwhelmed by the combinatorial explosion of potential configurations
Data Source
AI summary
Systems and methods for adapting a neural network model on a hardware platform. An example method includes obtaining neural network model information comprising decision points associated with a neural network, with one or more first decision points being associated with a layout of the neural network. Platform information associated with a hardware platform for which the neural network model information is to be adapted is accessed. Constraints associated with adapting the neural network model information to the hardware platform are determined based on the platform information, with a first constraint being associated with a processing resource of the hardware platform and with a second constraint being associated with a performance metric. A candidate configuration for the neural network is generated via execution of a satisfiability solver based on the constraints, with the candidate configuration assigns values to the plurality of decision points.