Unified AI Engine API for On-Premises Hardware Adaptability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software developers face challenges in working with AI due to complex toolkits, limited APIs, and constrained black-box solutions, making it difficult to effectively utilize AI for real-world problem-solving, and there is a shortage of data science experts to support AI development within the broader software development community.
Innovation Solution
An AI engine with multiple independent modules operating on a common API, supporting various training and prediction sources across different hardware environments, allowing for easy integration and adaptable operation on-premises or in the cloud, utilizing CPUs, GPUs, or DSPs, and enabling parallel processing to enhance training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a common API is used to support multiple different training sources and prediction sources on different customer computing hardware, then the adaptability and accessibility of the AI engine is improved, but the device complexity increases due to the need to handle diverse hardware configurations and software environments
Solution Approach 1:
The AI engine is designed with a universal common API that can interface with multiple different training sources and prediction sources across diverse customer computing hardware environments. This multi-functional design allows the same AI engine to operate on-premises, in the cloud, or in hybrid configurations without requiring separate implementations for each environment.
Solution Approach 2:
The common API acts as an intermediary layer between the AI engine and the diverse hardware/software environments. It abstracts the complexity of different hardware configurations (CPUs, GPUs, DSPs) and software platforms, providing a unified interface that simplifies integration while maintaining compatibility across multiple environments.
2Productivity
If parallel processing is implemented to enhance training efficiency, then the productivity of AI model training is improved, but the device complexity increases due to the need for multiple independent modules and coordinated processing
Solution Approach 1:
The AI engine is divided into multiple independent modules that can process training tasks in parallel. Each module can handle specific aspects of the training process independently, allowing for concurrent execution of multiple training operations and improving overall productivity without requiring complete redesign of the system architecture.
Solution Approach 2:
Multiple independent processing modules are combined within a unified AI engine framework that coordinates their operations. This merging allows parallel processing capabilities to be leveraged while maintaining a single, manageable system interface through the common API, thus improving productivity without proportionally increasing operational complexity.
Data Source
AI summary
The AI engine operates with the common API. The common API supports i) any of multiple different training sources and/or prediction sources installed on ii) potentially different sets of customer computing hardware in a plurality of on-premises' environments, where the training sources, prediction sources as well as the set of customer computing hardware may differ amongst the on-premises' environments. The common API via its cooperation with a library of base classes is configured to allow users and third-party developers to interface with the AI-engine modules of the AI engine in an easy and predictable manner through the three or more base classes available from the library. The common API via its cooperation with the library of base classes is configured to be adaptable to the different kinds of training sources, prediction sources, and the different sets of hardware found a particular on-premises environment.