Analog MAC Calibration for Accurate On-Device Weight Quantization
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Solution Overview
Problem
Existing neural network computing devices face challenges in maintaining inference accuracy while reducing resource requirements through quantization without the need for additional learning to compensate for errors in the quantization process.
Innovation Solution
A neural network computing device with an on-device quantizer that adjusts resistance values of variable resistance synaptic elements based on quantized weight data, using an analog MAC with a calibrator to calibrate gain and DC offset, and a controller to manage operation modes, allowing for efficient quantization and inference without additional learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantization of weight data is performed to reduce resources and increase inference speed, then resource consumption decreases and inference speed increases, but inference accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing calibration of the analog MAC before actual inference operations. The calibrator pre-adjusts gain and DC offset parameters using calibration weight data and calibration input data, storing optimized parameters in advance. This preliminary calibration ensures that subsequent quantized inference operations maintain high accuracy without requiring real-time adjustments during inference.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting quantization parameters including gain, DC offset, and bit-width based on calibration results. The system changes these parameters adaptively - using 8-bit quantization with calibrated gain and offset for normal operations, and switching to higher precision (16-bit or 32-bit) when accuracy requirements demand, thereby optimizing both speed and accuracy trade-offs.
2Reliability
If additional learning is performed to compensate for quantization errors, then inference accuracy is maintained, but device complexity and computational overhead increase
Solution Approach 1:
The patent replaces the mechanical/computational system of additional learning iterations with an electronic calibration system. Instead of performing repeated learning computations to compensate for quantization errors, the system uses an electronic calibrator that measures analog MAC characteristics and computes correction parameters (gain and DC offset) through direct calculation, substituting iterative learning with a more efficient calibration process.
Solution Approach 2:
The patent introduces an intermediary calibration component that mediates between the quantization process and inference accuracy. The calibrator acts as an intermediary by generating calibration weight data and calibration input data, measuring the analog MAC's response, and producing corrected quantized weight data that compensates for quantization errors without requiring additional learning operations.
3Device complexity
If fixed quantization parameters are used, then device complexity is reduced, but adaptability to different inference scenarios deteriorates
Solution Approach 1:
The patent applies dynamics by making quantization parameters adaptive rather than fixed. The system dynamically adjusts gain, DC offset, and bit-width parameters based on calibration results and inference requirements. The controller can switch between different quantization configurations (e.g., 8-bit, 16-bit, 32-bit) and adjust calibration parameters in real-time, enabling the system to adapt to various inference scenarios while maintaining manageable device complexity through automated calibration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enhances reliability and flexibility by enabling re-quantization and improving performance through dynamic adjustment of quantized weight data, maintaining accuracy and reducing resource consumption.
Implementation Method 1
a neural network accelerator including an analog MAC
Implementation Method 2
The analog MAC may include variable resistance synaptic elements
Implementation Method 3
a calibrator that calibrates a gain and a DC offset of the analog MAC
Implementation Method 4
an on-device quantizer that reads the weight data from the memory, receives the gain and the DC offset from the gain and offset calculator, generates quantized weight data by quantizing the weight data, based on the gain and the DC offset
Data Source
AI summary
Disclosed is a neural network computing device. The neural network computing device includes a neural network accelerator including an analog MAC, a controller controlling the neural network accelerator in one of a first mode and a second mode, and a calibrator that calibrating a gain and a DC offset of the analog MAC. The calibrator includes a memory storing weight data, calibration weight data, and calibration input data, a gain and offset calculator reading the calibration weight data and the calibration input data from the memory, inputting the calibration weight data and the calibration input data to the analog MAC, receiving calibration output data from the analog MAC, and calculating the gain and the DC offset of the analog MAC, and an on-device quantizer reading the weight data, receiving the gain and the DC offset, generating quantized weight data, based on the gain and the DC offset.