Neural Network Weight Quantization With Fixed-Point Scaling
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Solution Overview
Problem
Large deep learning neural networks require significant computational resources and energy, making them unsuitable for deployment in resource-constrained settings, such as wearable devices without a permanent cloud connection, due to their high precision floating-point weight requirements.
Innovation Solution
A method to convert floating-point weighting factors to fixed-point weighting factors by selecting a scaling factor that minimizes the mean square error, using a predetermined base, and applying it to scale the weights during the training stage, allowing for efficient fixed-point operations in neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If floating-point weighting factors are used in neural networks, then classification accuracy and model performance are maintained, but computational resources and energy consumption increase significantly
Solution Approach 1:
The patent changes the numerical representation parameter of weighting factors from floating-point to fixed-point format. This parameter change reduces the computational complexity and energy consumption while maintaining sufficient precision for classification tasks. The fixed-point representation uses a predetermined base and scaling factor to preserve the essential information needed for accurate predictions.
Solution Approach 2:
The patent extracts only the necessary precision requirements for neural network operations by using quantization. Instead of maintaining full floating-point precision, it extracts the essential information through fixed-point approximation, removing the excess computational overhead associated with full-precision floating-point arithmetic while retaining sufficient accuracy for edge device deployment.
2Reliability
If large deep learning networks are deployed, then classification performance improves, but device resource requirements and memory usage increase
Solution Approach 1:
The patent applies parameter changes by transitioning from floating-point to fixed-point representation, which reduces the memory footprint of weighting factors. This allows larger networks to be deployed within the same memory constraints, or equivalently, achieves the same network size with reduced memory requirements, enabling deployment on resource-constrained edge devices.
Solution Approach 2:
The patent creates a simplified copy of the floating-point weighting factors in fixed-point format. This copy maintains the essential functional characteristics needed for classification while occupying less memory space, allowing the neural network to operate effectively with reduced resource requirements.
3Measurement precision
If floating-point operations are performed, then computational accuracy is maintained, but processing speed and efficiency decrease
Solution Approach 1:
The patent substitutes floating-point arithmetic operations with fixed-point arithmetic operations. Fixed-point operations can be implemented more efficiently in hardware, replacing complex floating-point units with simpler arithmetic logic that operates on integer representations. This substitution maintains sufficient computational accuracy while significantly improving processing speed and efficiency on resource-constrained devices.
Solution Approach 2:
By changing the numerical representation parameter from floating-point to fixed-point, the patent enables more efficient computational operations. The fixed-point format allows for simplified arithmetic operations that are faster and more energy-efficient, while the quantization process preserves the necessary precision for accurate neural network inference.
4Use of energy by moving object
If quantization is applied to reduce precision, then resource consumption decreases, but model accuracy may deteriorate
Solution Approach 1:
The patent carefully manages the precision parameter through controlled quantization. By using a predetermined base and optimizing the scaling factor, it changes the representation parameter to fixed-point format in a way that minimizes accuracy loss while achieving significant reductions in energy consumption and resource usage.
Solution Approach 2:
The patent employs feedback mechanisms during the quantization process to monitor and maintain accuracy. The scaling factor and quantization parameters are selected to minimize the error between the original floating-point weights and the quantized fixed-point weights, ensuring that model accuracy is preserved despite the reduction in precision.
Data Source
AI summary
A system and method for generating and using fixed-point operations for neural networks includes converting floating-point weighting factors into fixed-point weighting factors using a scaling factor. The scaling factor is defined to minimize a cost function and the scaling factor is derived from a set of multiples of a predetermined base. The set of possible scaling function is defined to reduce the computational effort for evaluating the cost function for each of a number of possible scaling factors. The system and method may be implemented in one or more controllers that are programmed to execute the logic.