Low-Bit Neural Network Normalization With Flexible Sign Bits
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Solution Overview
Problem
Machine learning models face challenges in deploying on diverse devices due to computationally expensive operations, particularly in normalization processes, leading to saturation and reduced accuracy when using limited bit widths, which can be exacerbated by unnecessary use of sign bits for non-negative results.
Innovation Solution
Implementing a flexible sign bit approach that dynamically determines whether to use a sign bit based on the operation type, allowing all bits to represent non-negative results without a sign bit for operations like normalization, and using enhanced instructions for square operations to differentiate beyond saturation limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sign bit is used for all operations in low-bit neural networks, then negative values can be represented, but the representable range for non-negative results is reduced and saturation occurs more frequently
Solution Approach 1:
The patent applies dynamics by making the sign bit flexible rather than fixed. The system dynamically determines whether to use a sign bit based on the operation type: for operations that produce only non-negative results (like normalization), the sign bit is omitted to maximize representable range, while for operations that may produce negative results, the sign bit is used. This dynamic adaptation resolves the contradiction between maintaining accuracy and maximizing representable range.
Solution Approach 2:
The patent changes the parameter of bit allocation by operation type. Instead of using a fixed bit format for all operations, the system adjusts the number of bits allocated for the sign based on the specific operation being performed. For normalization operations, the sign bit parameter is set to 0, while for other operations it may be set to 1, thereby optimizing the representable range for each operation type.
2Use of energy by moving object
If limited bit widths are used in low-bit neural networks, then resource consumption is reduced, but saturation occurs and accuracy decreases
Solution Approach 1:
The patent applies local quality by optimizing the bit representation locally for each operation type rather than using a uniform representation throughout the network. For normalization operations that produce non-negative results, the system uses a specialized representation without sign bits, maximizing the effective range for these specific operations while maintaining overall low-bit resource efficiency.
3Adaptability or versatility
If normalization operations are performed with sign bits in low-bit formats, then negative values can be represented, but the effective range for non-negative normalization results is reduced
Solution Approach 1:
The patent extracts the sign bit from the bit representation for normalization operations. Since normalization operations produce only non-negative results, the sign bit is removed entirely for these operations, allowing all available bits to represent the magnitude of the result. This extraction of the unnecessary sign component maximizes the effective range and precision for normalization operations.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning model operations. Embodiments include accessing a value encoded with a sign bit and performing an operation using the value within a machine learning model. A result of the performance of the operation may be encoded with no sign bit based on a type of the operation.