Neural Network Quantization Optimization via Hardware-Aware Bit-Shift Configuration
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Solution Overview
Problem
Deep neural networks (DNNs) deployed on edge devices face reduced model accuracy due to hardware-specific constraints and limitations, which hinder effective quantization processes, leading to increased power consumption and model size without significant improvements in inference speed.
Innovation Solution
A system and method that formulates neural network quantization as an optimization problem with constraints, using bit-shift operations to minimize quantization errors by configuring hardware devices like MAC arrays, optimizing bit-shifts to reduce model size and power consumption while maintaining inference speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization is applied to reduce model size and power consumption, then model size and power consumption are reduced, but model accuracy decreases due to hardware-specific constraints
Solution Approach 1:
The patent changes the parameters of quantization by formulating it as an optimization problem with multiple parameters including bit-width, scaling factors, and rounding modes. The system searches for optimal parameter combinations that minimize quantization error while satisfying hardware constraints, thereby improving accuracy without sacrificing the size reduction benefits of quantization.
Solution Approach 2:
The patent introduces dynamic adaptation by allowing different quantization configurations for different layers or operations within the neural network. The optimization process dynamically determines the best quantization parameters for each specific operation based on its sensitivity to quantization error and hardware capabilities, rather than applying a uniform quantization scheme throughout.
2Adaptability or versatility
If hardware-specific quantization constraints are applied, then quantization can be implemented on edge devices, but quantization accuracy decreases
Solution Approach 1:
The patent applies local quality by treating different parts of the neural network with different quantization strategies. Each layer or operation is analyzed individually to determine its specific quantization requirements, allowing critical layers to maintain higher precision while less sensitive layers use more aggressive quantization, thus optimizing overall accuracy while maintaining hardware compatibility.
Solution Approach 2:
The patent segments the quantization optimization problem into multiple independent sub-problems, one for each layer or operation type. This segmentation allows the system to independently optimize quantization parameters for each segment while considering hardware constraints, and then combine the results to achieve overall optimal performance on the target hardware.
3Use of energy by stationary object
If model size is reduced through quantization, then power consumption decreases, but inference speed improvement is limited
Solution Approach 1:
The patent changes quantization parameters to find the optimal balance between model size and inference speed. By exploring different bit-widths and precision levels, the system identifies configurations that achieve sufficient size reduction for power savings while maintaining precision levels that preserve inference speed, avoiding excessive quantization that would hurt performance.
Data Source
AI summary
Embodiments of this disclosure provide a system and method for reducing quantization errors at an output of a hardware device functioning as a neural network structure. During operation, the system can obtain type information associated with the neural network structure and construct a hardware model of the neural network structure based on the obtained type information, the hardware model comprising one or more paths for performing arithmetic operations. The system can formulate an optimization problem to reduce the quantization errors based on the constructed hardware model, the optimization problem being defined by an objective function and a set of constraints. The system can solve the optimization problem and configure the hardware device based on a solution to the optimization problem, thereby reducing the quantization errors at the output of the hardware device.