Hardware Quantization Optimization for Neural Network Edge Devices
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Solution Overview
Problem
Deep neural networks (DNNs) deployed on edge devices face challenges in reducing model size and power consumption without compromising inference speed, often resulting in decreased model accuracy due to hardware-specific constraints on quantization processes.
Innovation Solution
A mathematical modeling and optimization system that configures multiplier-accumulator (MAC) units to perform neural network parameter quantization and execution layer by layer, formulating arithmetic operations as an optimization problem to minimize accuracy losses and optimize quantization precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization techniques are used to reduce model size, then model size and power consumption are reduced, but model accuracy decreases
Solution Approach 1:
The patent changes the parameters of quantization by formulating it as an optimization problem with adjustable variables including bit-width, scaling factors, and rounding modes. By systematically varying these parameters and evaluating their impact on accuracy, the system identifies optimal quantization configurations that minimize accuracy loss while maintaining model size reduction benefits
Solution Approach 2:
The patent introduces dynamic adjustment of quantization parameters during the optimization process. The system adaptively selects different quantization strategies for different layers and operations based on their sensitivity to quantization, allowing the quantization precision to be dynamically optimized rather than uniformly applied
2Adaptability or versatility
If hardware-specific algorithmic operations are imposed on quantization process, then device compatibility is improved, but quantization accuracy decreases
Solution Approach 1:
The patent segments the quantization optimization problem into multiple independent sub-problems, each corresponding to a specific hardware operation or device type. By formulating separate optimization problems for different MAC unit configurations and hardware constraints, the system can find device-specific optimal quantization parameters without compromising overall accuracy
Solution Approach 2:
The patent applies different quantization strategies and precision levels to different parts of the neural network based on local hardware capabilities and operational requirements. Critical layers receive higher precision treatment while less sensitive layers use aggressive quantization, optimizing the balance between device compatibility and quantization accuracy
3Measurement precision
If optimization is performed to minimize accuracy losses, then quantization precision is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial optimization by focusing computational resources on optimizing only the most critical quantization parameters and layers that have the greatest impact on accuracy. Rather than exhaustively optimizing all possible parameters, the system identifies and optimizes the key variables that provide the most significant accuracy improvement per unit of computational cost
Data Source
AI summary
In one aspect, a system that can minimize quantization accuracy losses when implementing a neural network node on an edge device is disclosed. During operation, the system identifies a set of adjustable parameters in the edge device. The set of adjustable parameters include a set of registers of the edge device. The system then models the quantization of parameters and output of the neural network node on the edge device as an optimization problem by formulating at least an objective function, wherein the objective function is a function of the set of adjustable parameters. Next, the system solves the optimization problem by identifying a set of values for the set of adjustable parameters that satisfies the objective function. The system subsequently programs the edge device by configuring the set of adjustable parameters with the set of identified values. The programmed edge device implements the neural network node with improved precision.