Resistive Processing Unit Matrix-Vector Calibration
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Solution Overview
Problem
Analog resistive processing systems for neuromorphic computing face challenges in calibrating matrix-vector operations due to non-idealities in RPU hardware, leading to disparities between forward and backward pass training operations.
Innovation Solution
A system and method for automatically calibrating matrix-vector operations by generating calibration parameters using a processor and a resistive processing unit with programmable memory devices, which includes a weight extraction process to determine effective forward and backward weight matrices, and a calibration parameters computation process to apply scaling and offset corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If matrix-vector operations are performed on RPU hardware without calibration, then computational speed is improved, but weight value accuracy deteriorates due to hardware non-idealities
Solution Approach 1:
The patent performs calibration operations before actual matrix-vector computations to determine calibration parameters (scaling factors and offset corrections) that compensate for hardware non-idealities. This preliminary characterization of the RPU array allows subsequent computations to use corrected weight values, maintaining both speed and accuracy.
Solution Approach 2:
The calibration process measures actual weight values read from the RPU array and compares them against expected values, then uses this feedback to compute correction parameters. These parameters are applied to future operations to compensate for systematic errors in the hardware, creating a closed-loop correction system.
2Reliability
If calibration parameters are generated for both forward and backward pass operations, then training consistency is improved, but system complexity increases
Solution Approach 1:
The patent separates calibration into two distinct parameter sets: one for forward pass operations and another for backward pass operations. This segmentation allows each pass type to be optimized independently, ensuring training consistency while maintaining clear, manageable calibration procedures for each operational mode.
Solution Approach 2:
The calibration framework uses a unified approach that generates parameters applicable to both forward and backward pass operations through a single systematic process. The same calibration infrastructure and methodology serve multiple functions, reducing overall system complexity despite the dual-parameter requirement.
3Measurement precision
If calibration processes are performed on RPU arrays, then operational accuracy is improved, but processing time increases
Solution Approach 1:
The calibration process is performed once as a preliminary step before actual training computations begin. Although this initial calibration consumes processing time, the resulting calibration parameters are reused for all subsequent matrix-vector operations, making the time cost amortized over many accurate computations.
Solution Approach 2:
The system performs self-calibration by automatically characterizing its own hardware non-idealities and generating correction parameters without external intervention. This self-service approach eliminates the need for manual calibration procedures and integrates the calibration process seamlessly into the system initialization.
Data Source
AI summary
A system comprises a processor, and a resistive processing resistive processing unit coupled to the processor. The resistive processing unit comprises an array of cells, wherein the cells respectively comprise resistive memory devices, wherein at least a portion of the resistive memory devices are programmable to store weight values of a given matrix in the array of cells. The processor is configured to store the given matrix in the array of cells of the resistive processing unit, and perform a calibration process to generate a first set of calibration parameters for calibrating forward pass matrix-vector multiplication operations performed on the stored matrix in the array of cells of the resistive processing unit, and a second set of calibration parameters for calibrating backward pass matrix-vector multiplication operations performed on a transpose of the stored matrix in the array of cells of the resistive processing unit.