Analog Compute-in-Memory Endurance Management via Drift-Aware Write Distribution
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Solution Overview
Problem
Conventional analog compute-in-memory technologies face challenges in preventing computational errors due to the drift phenomenon, which requires periodic refresh or AI retraining, negatively impacting memory cell endurance and incurring significant overhead.
Innovation Solution
A method is introduced to manage the endurance of memory cells in analog compute-in-memory systems by determining the optimal arrangement of matrices across analog processing elements, performing write operations in a way that distributes the number of write operations evenly across memory cells, and using an endurance manager to track and manage the write operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic refresh of memory cell values is performed to prevent computational errors, then reliability is improved, but memory cell endurance deteriorates
Solution Approach 1:
The system performs preliminary drift detection and assessment before computational errors occur. By monitoring drift levels and predicting when they will reach error-causing thresholds, the system can plan refresh operations in advance, targeting only the memory cells that need refreshing rather than performing periodic refresh of all cells, thus improving reliability while preserving endurance.
Solution Approach 2:
Instead of uniformly refreshing all memory cells, the system applies refresh operations locally to specific memory cells or regions that have been identified as having drifted beyond acceptable thresholds. This localized approach ensures computational accuracy for affected cells while minimizing unnecessary write operations on cells that remain within acceptable drift margins, thereby extending overall memory cell endurance.
2Reliability
If AI retraining is performed to compensate for drift phenomenon, then reliability is improved, but computational overhead increases
Solution Approach 1:
The system extracts and addresses only the specific drift problem in memory cells rather than performing comprehensive AI retraining. By identifying and refreshing only the affected memory cells that contribute to drift, the system eliminates the need for time-consuming full AI model retraining while still maintaining computational accuracy, thus improving reliability without incurring significant overhead.
Solution Approach 2:
Instead of performing complete AI retraining, the system applies partial correction by refreshing only the subset of memory cells that have drifted. This partial action is sufficient to maintain computational accuracy for the affected operations without the excessive time cost of retraining the entire AI model, thereby achieving reliability improvement with minimal overhead.
3Adaptability or versatility
If matrix data is frequently updated in analog processing elements, then adaptability is improved, but the number of write operations increases
Solution Approach 1:
The system performs preliminary assessment of drift levels and update requirements before executing write operations. By predicting which matrix data updates are necessary based on drift monitoring, the system can avoid unnecessary write operations while still maintaining the required adaptability and data freshness, thus extending memory cell lifespan without sacrificing update capability.
Solution Approach 2:
The system applies updates locally to only those memory cells containing matrix data that has drifted beyond acceptable thresholds. This selective update approach maintains the adaptability of the analog processing elements by ensuring necessary data freshness while minimizing the total number of write operations, thereby preserving memory cell lifespan.
Data Source
AI summary
Disclosed herein are an apparatus and method for managing memory cell endurance of an analog computing system. The apparatus for managing memory cell endurance of an analog computing system includes memory configured to store at least one program and a processor configured to execute the program, wherein the program is configured to determine analog processing elements in which matrix data is to be arranged and write matrix data to the determined analog processing elements, and as an update for matrix data stored in the analog processing elements occurs, write updated matrix data to memory cells selected based on a number of write operations on internal memory cells of each of the analog processing elements.