Analog Processor Training With Block Floating-Point and Noise-Aware Learning
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Solution Overview
Problem
Analog processors face limitations due to lower precision and susceptibility to noise, making them unsuitable for high-precision computing tasks in machine learning models, such as neural networks, despite offering speed and energy efficiency advantages.
Innovation Solution
Implementing an adaptive block floating-point (ABFP) representation for matrices and incorporating noise-aware training techniques, such as quantization-aware training (QAT), to enhance the performance of analog processors in matrix operations for machine learning model training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an analog processor is used for matrix operations in machine learning, then speed and energy efficiency are improved, but precision and noise resistance deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming the data representation format from standard floating-point to block floating-point format. This involves changing the numerical representation parameters (exponent and mantissa organization) to better suit the analog processor's characteristics, thereby maintaining precision while utilizing the analog processor's speed advantages for matrix operations in machine learning
Solution Approach 2:
The patent implements preliminary action through quantization-aware training (QAT), where the model is trained in advance with simulated quantization effects. This preliminary training phase prepares the model to compensate for precision losses that will occur during actual analog processing, ensuring optimal performance when deployed on the analog processor
2Use of energy by moving object
If an analog processor is used for matrix operations in machine learning, then energy efficiency is improved, but noise susceptibility increases
Solution Approach 1:
The patent applies preliminary action by implementing noise modeling during the training phase. Synthetic noise is injected into the training process to simulate analog processor characteristics, allowing the model to learn robustness against noise before actual deployment. This prepares the system to handle noise susceptibility while maintaining energy efficiency benefits of analog processing
Solution Approach 2:
The patent converts the harmful effect of noise susceptibility into a benefit through adaptive block floating-point representation. By carefully designing the block floating-point format with appropriate exponent and mantissa allocations, the system transforms potential precision losses into a structured representation that better matches analog processor characteristics, thereby converting a disadvantage into an optimization opportunity
3Device complexity
If standard floating-point representation is used on an analog processor, then computational simplicity is maintained, but performance loss occurs due to lower precision
Solution Approach 1:
The patent changes the numerical representation parameters from standard IEEE floating-point to a customized block floating-point format optimized for analog processors. This involves reorganizing how exponents and mantissas are stored and processed, creating a representation that better matches the analog hardware's natural precision characteristics while maintaining computational efficiency
Solution Approach 2:
The patent applies local quality by dividing the floating-point representation into distinct blocks (exponent block and mantissa block) with different precision allocations. This allows different parts of the numerical representation to have different precision characteristics, optimizing the balance between computational simplicity and performance for specific analog processor architectures
Data Source
AI summary
Described herein are techniques of training a machine learning model and performing inference using an analog processor. Some embodiments mitigate the loss in performance of a machine learning model resulting from a lower precision of an analog processor by using an adaptive block floating-point representation of numbers for the analog processor. Some embodiments mitigate the loss in performance of a machine learning model due to noise that is present when using an analog processor. The techniques involve training the machine learning model such that it is robust to noise.