Microkernel-Based AI Network Software Optimization
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Solution Overview
Problem
Existing software implementation methods for artificial intelligence networks lack efficiency and flexibility, as they either require manual rewriting of kernel code for different hardware platforms or result in significant performance inefficiencies when made hardware-agnostic.
Innovation Solution
The system generates kernels for AI network operations by configuring input and output data, detecting specific hardware components, selecting and invoking hardware-specific microkernels, and compiling software code using a Just-In-Time compiler, allowing for efficient execution across various hardware components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual rewriting of kernel code is performed for each hardware platform, then hardware-specific optimization is achieved, but development time and complexity increase significantly
Solution Approach 1:
The patent segments the kernel implementation into hardware-agnostic high-level operations and hardware-specific microkernels. The high-level ops define the computational logic independently of hardware, while only the microkernel level requires hardware-specific optimization. This segmentation allows most code to remain unchanged across platforms, reducing rewriting time while maintaining optimization efficiency.
Solution Approach 2:
The patent introduces a microkernel as an intermediary layer between hardware and high-level operations. The microkernel serves as a mediator that translates hardware-specific instructions into hardware-agnostic operations, enabling efficient hardware utilization without requiring direct rewriting of high-level AI network code for each platform.
2Adaptability or versatility
If hardware-agnostic code is used to improve flexibility, then cross-platform compatibility is achieved, but execution efficiency decreases significantly
Solution Approach 1:
The patent implements dynamic selection of microkernels based on the target hardware platform. The system can dynamically choose different microkernels for different hardware architectures (CPU, GPU, TPU) while maintaining the same high-level operation interface. This dynamic approach enables both hardware compatibility and execution efficiency by adapting the low-level implementation to the specific platform.
Solution Approach 2:
The microkernel acts as an intermediary that enables hardware-agnostic high-level operations to run efficiently on specific hardware. The microkernel translates the hardware-agnostic operation into optimized hardware-specific instructions, thereby achieving both cross-platform compatibility and high execution efficiency without sacrificing either aspect.
3Ease of manufacture
If high-level hardware-agnostic programming is used, then code portability is improved, but performance optimization capability is reduced
Solution Approach 1:
The patent segments the programming hierarchy into high-level hardware-agnostic operations for portability and low-level hardware-specific microkernels for optimization. This segmentation allows developers to write portable high-level code while the system automatically or manually optimizes the microkernel level for specific hardware, achieving both ease of porting and precision of optimization.
Solution Approach 2:
The patent changes the implementation parameters at the microkernel level while maintaining the same high-level operation interface. By adjusting parameters such as data types, memory layouts, and computational algorithms at the microkernel level, the system can optimize performance for specific hardware without changing the portable high-level operation definition, thus maintaining both portability and optimization precision.
Data Source
AI summary
Disclosed are systems and methods related to providing for the optimized software implementations of artificial intelligence (“AI”) networks. The system receives operations (“ops”) consisting of a set of instructions to be performed within an AI network. The system then receives microkernels implementing one or more instructions to be performed within the AI network for a specific hardware component. Next, the system generates a kernel for each of the operations. Generating the kernel for each of the operations includes configuring input data to be received from the AI network; detecting a specific hardware component to be used; selecting one or more microkernels to be invoked by the kernel based on the detection of the specific hardware component; and configuring output data to be sent to the AI network as a result of the invocation of the microkernel(s).