Virtualized AI Hardware Components Using Intermediate Representation
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Solution Overview
Problem
Existing AI hardware accelerators face inefficiencies due to heterogeneity in system architectures, requiring developers to maintain different tools and platforms, and end users to learn and deploy different environments, leading to inefficiencies in hardware utilization and application development.
Innovation Solution
A system and method for virtualizing AI hardware components using a guest system that generates an intermediate representation (IR) which is translated into hardware operations by a host system, enabling unified interface and operation across different hardware components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If different hardware components (GPU, DSP, ASIC, FPGA) are used to implement AI applications, then hardware-specific optimization and performance are improved, but system heterogeneity increases requiring developers to maintain different tools and platforms
Solution Approach 1:
The patent introduces an intermediate representation (IR) layer that acts as a mediator between diverse AI hardware components and AI applications. The IR provides a unified interface that abstracts hardware-specific details, allowing applications to run on different hardware without requiring developers to maintain separate tools and platforms for each hardware type.
Solution Approach 2:
The intermediate representation serves as a universal interface that can be translated to multiple different hardware backends. This multi-functionality allows the same AI application to be deployed across GPU, DSP, ASIC, and FPGA platforms without modification, resolving the heterogeneity issue while maintaining hardware-specific optimization capabilities.
2Productivity
If AI applications are developed for specific hardware platforms, then hardware utilization efficiency is improved, but deployment flexibility and portability across different platforms deteriorate
Solution Approach 1:
The intermediate representation acts as a portable intermediary that decouples the AI application from hardware-specific details. Applications compile to IR which can then be translated to any supported hardware backend, maintaining both hardware utilization efficiency through optimized backends and platform portability through the universal IR interface.
Solution Approach 2:
The compilation process is segmented into two independent stages: first, the AI application is compiled to an intermediate representation that captures the computational logic; second, the IR is translated to hardware-specific instructions. This segmentation allows the same IR to be deployed on different hardware platforms while maintaining optimization for each platform's characteristics.
3Productivity
If end users learn and deploy different environments for different hardware, then hardware-specific optimization is achieved, but user effort and deployment complexity increase
Solution Approach 1:
The intermediate representation serves as a universal deployment interface that eliminates the need for end users to learn and configure different environments for different hardware. Users deploy applications through the unified IR interface, while the system automatically handles hardware-specific optimization through backend translation, significantly reducing deployment effort and complexity.
Data Source
AI summary
A computing environment can include a host system that maintains a guest system, and a hardware component configured to implement artificial intelligence (“AI”) methods of processing and analyzing date. The guest system can provide a virtual computing environment that receives a request to implement an AI application, and utilize a framework and a guest library to convert data from the AI application into an intermediate representation (“IR”). The host system can receive the IR with a virtual device (“VD”), and utilize an IR backend to translate the IR into hardware operations for the hardware component. Translated hardware operations can be provided to, and carried out by, the hardware component to provide an implementation of the AI application. Results of the hardware operations can be transmitted from the VD of the host system to a VD driver of the guest system, virtualizing the hardware component relative to the guest system.