Neural Network Optimization Engine for Edge Device Deployment
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Solution Overview
Problem
Existing technologies face challenges in efficiently deploying machine learning models on edge devices with limited processing and memory resources, leading to suboptimal execution of neural networks due to mismatched hardware capabilities and resource constraints.
Innovation Solution
An optimization engine analyzes the neural network architecture, identifies platform-specific optimizations such as kernel padding, memory allocation strategies, and quantization, to compile executable files that align with the edge device's computational and memory resources, enabling efficient execution on target-specific platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models are deployed on edge devices with limited resources, then local real-time processing capability is improved, but execution efficiency deteriorates due to resource constraints
Solution Approach 1:
The patent applies parameter changes by transforming the neural network model parameters (precision, data types, computational operations) to match the edge device's hardware capabilities. The optimization engine analyzes the target device's processor architecture, memory constraints, and instruction set to automatically adjust model parameters such as converting floating-point operations to integer operations, applying quantization, and selecting appropriate precision levels, thereby resolving the contradiction between maintaining real-time processing capability and achieving efficient execution on resource-constrained devices.
Solution Approach 2:
The patent employs segmentation by dividing the neural network model into multiple segments or layers that can be selectively optimized and executed. The optimization engine partitions the model computation into discrete operations that can be mapped to specific hardware accelerators, GPUs, or CPU cores available on the edge device, enabling parallel execution and improving overall productivity while maintaining real-time processing capability.
2Speed
If cloud processing is used instead of local processing, then computational speed is improved, but network bandwidth limitations and data exposure risks worsen
Solution Approach 1:
The patent applies self-service by enabling the edge device to independently execute machine learning inference tasks using its own local processing resources. The optimization engine compiles and optimizes the model for the specific edge device, allowing it to perform computations autonomously without requiring continuous cloud connectivity. This resolves the contradiction by providing local real-time processing capability while eliminating network bandwidth limitations and reducing data exposure risks, as sensitive data remains on the edge device.
3Productivity
If neural network models are optimized for specific hardware platforms, then execution efficiency on target devices is improved, but device complexity and optimization process complexity worsen
Solution Approach 1:
The patent introduces an optimization engine as an intermediary between the neural network model and the target edge device. This intermediary automatically analyzes the target device's hardware architecture, memory constraints, and computational capabilities, then transforms the generic model into an optimized version tailored for that specific platform. The optimization engine handles the complexity of platform-specific optimizations (kernel selection, memory allocation, parallelization strategies) automatically, resolving the contradiction by improving execution efficiency without requiring manual intervention or increasing user-facing device complexity.
Data Source
AI summary
Implementations disclosed describe methods and systems to perform the methods of deploying and executing machine learning models on target-specific computational platforms. Optimization techniques include but are not limited to alignment of kernel operations with hardware instructions of a target processing device, reduction of kernel dimensions near boundaries of data, efficient reuse of a small number of memory components during neural network operations, run-time quantization of data and neural network parameters, and other methods.