Template-Based AI Model Compilation for ASICs
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Solution Overview
Problem
Developing new AI models often requires writing new code, which can lead to errors, delays, and increased costs due to the need for manual coding using machine learning or artificial intelligence frameworks like PyTorch or TensorFlow.
Innovation Solution
The method involves using a template file that identifies a base AI model and structural parameters for a new AI model, allowing a compiler to modify compilation data and generate executable code for the new AI model on a model-specific chipset, thereby reducing the need for manual coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new AI models are coded from scratch using AI frameworks, then developers have full control over model creation, but this introduces errors, delays release, and increases cost
Solution Approach 1:
The patent uses template models as reusable blueprints that can be copied and modified to create new AI models. Instead of coding each model from scratch, developers can start with pre-defined templates and make necessary adjustments, significantly reducing errors and development time while maintaining the ability to create custom models.
Solution Approach 2:
The patent implements pre-configured template models that have already been structured and optimized before use. These templates contain pre-defined architectures, layers, and configurations that are prepared in advance, allowing developers to skip the initial coding phase and directly customize models for their specific needs.
2Productivity
If new AI models are coded from scratch using AI frameworks, then developers can create custom models, but this delays release and increases cost
Solution Approach 1:
By copying and modifying existing template models, developers can rapidly create new AI models without starting from scratch. This approach maintains the ability to create custom models while dramatically reducing development time and accelerating release schedules.
Solution Approach 2:
The patent allows developers to customize template models by modifying parameters such as layer configurations, activation functions, and hyperparameters. This parameter-based customization enables rapid model adaptation without requiring extensive recoding, thus improving productivity while maintaining model specificity.
3Adaptability or versatility
If new AI models are coded from scratch using AI frameworks, then developers have flexibility in model design, but this increases cost
Solution Approach 1:
Reusable template models serve as cost-effective foundations that can be copied and adapted for multiple projects. This approach maintains model customization capabilities while reducing development costs by eliminating redundant coding efforts and leveraging existing optimized structures.
Solution Approach 2:
The system enables model adaptation through parameter modification rather than complete recoding. Developers can adjust template parameters to suit different applications, maintaining versatility and adaptability while significantly reducing the costs associated with creating custom models from scratch.
Data Source
AI summary
Embodiments herein describe using template files for translating an existing (or base) AI model into a new AI model for a model-specific chipset. That is, instead of requiring a developer to use an AI framework to prepare new code for the new AI model, a compiler can receive a template file which indicates a base AI model (e.g., an AI model that has already been executed on the model-specific chipset) and structural parameters for the new AI model. The compiler can use the structural parameters to modify compilation data corresponding to the base AI model. The compiler can then use the modified compilation data to create code for the new AI model that executes on the model-specific chipset.