Static Analysis for Deep Learning Model Optimization
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Solution Overview
Problem
Existing deep learning frameworks face challenges in optimizing deep neural networks for inference on edge devices with varying hardware resources and architectures, leading to difficulties in achieving efficient model deployment and execution.
Innovation Solution
The proposed solution involves a unified end-to-end system that uses static analysis with a hardware-based analytical cost model and a search algorithm to optimize deep neural networks for efficient inference on different hardware platforms, including CPUs and GPUs, without requiring real hardware for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic profiled cost models are used to optimize deep neural networks, then optimization accuracy may improve, but compilation time increases significantly
Solution Approach 1:
The patent pre-computes and stores hardware profiled cost model data during system initialization or offline processing. This preliminary action allows the actual optimization to use pre-available data, reducing real-time computation requirements and speeding up compilation while maintaining accurate hardware-specific optimization guidance
2Productivity
If hardware-specific optimization is performed, then inference performance improves, but device complexity increases
Solution Approach 1:
The patent creates hardware-specific optimization profiles by copying and adapting a general optimization framework to specific hardware architectures. Instead of building complex hardware-specific optimizers from scratch, the system copies the general framework and applies hardware-specific cost models and parameters, reducing overall system complexity while maintaining hardware-optimized performance
Solution Approach 2:
The patent maintains a unified optimization framework and achieves hardware-specific optimization by changing parameters such as cost model coefficients, hardware capabilities thresholds, and optimization targets. This parameter-based adaptation avoids the complexity of restructuring the entire optimization system for each hardware platform
3Measurement precision
If real hardware is used for optimization, then optimization accuracy improves, but device resources are consumed
Solution Approach 1:
The patent introduces hardware emulators or simulated hardware models as intermediaries between the optimization system and actual physical hardware. These intermediaries provide accurate hardware-specific cost information and performance predictions without requiring direct access to real hardware, thereby maintaining optimization accuracy while reducing physical device resource consumption
Solution Approach 2:
The patent creates virtual copies of hardware characteristics through software-based hardware models and emulators. These copies replicate hardware behavior, cache hierarchies, and performance characteristics without requiring actual physical hardware, enabling accurate optimization through simulation rather than direct hardware execution
4Adaptability or versatility
If comprehensive hardware support is implemented, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent designs a universal optimization framework that can handle multiple hardware architectures through a single unified codebase. The framework uses parameterizable cost models and configurable hardware capability detection to adapt to different platforms (CPUs, GPUs, FPGAs) without requiring separate optimization systems for each architecture, thereby improving adaptability while controlling complexity
Solution Approach 2:
The patent implements dynamic hardware capability detection and adaptive optimization selection. The system automatically detects target hardware characteristics at runtime and dynamically adjusts optimization parameters, cost models, and execution strategies accordingly. This dynamic adaptation enables comprehensive hardware support through a single flexible system rather than multiple static systems
Data Source
AI summary
Techniques for optimizing and deploying deep neural network (CNN) machine learning models for inference using static analysis are described. A method includes obtaining a deep neural network (DNN) machine learning (ML) model, generating an intermediate representation for the ML model, the intermediate representation including one or more nodes corresponding to one or more operators utilized by the ML model, identifying, for at least one node of the intermediate representation, an optimized schedule for at least one operator corresponding to the at least one node using a static analysis that is based on a hardware-specific cost model, generating an optimized intermediate representation using the optimized schedule that is optimized for execution on a hardware platform, and generating code corresponding to the ML model based at least in part on the optimized intermediate representation, wherein the code is specific to the hardware platform.