Decoding System for Redundant Analog Neural Memory Access
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Solution Overview
Problem
Existing analog neural memory systems lack an effective decoding system to provide redundancy during programming, erase, read, and neural read operations, especially when multiple faulty rows or columns occur.
Innovation Solution
The proposed solution involves a decoding system that allows access to redundant non-volatile memory cells in place of faulty rows or columns, enabling simultaneous activation of all redundant rows or columns during neural read operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional decoding systems are used, then individual row or column access is enabled, but simultaneous activation of all redundant rows or columns during neural read operations is not supported
Solution Approach 1:
The decoding system dynamically switches between conventional decoding mode (for individual row/column access) and neural read mode (for simultaneous activation of all redundant rows/columns). The system adapts its decoding behavior based on the operation type, enabling both conventional and neural read operations with appropriate redundancy handling for each mode.
Solution Approach 2:
The decoding system is designed to perform multiple functions: it can decode individual row/column addresses for conventional operations and simultaneously activate all redundant rows/columns for neural read operations. This multi-functional decoding approach allows a single system to support both conventional memory access patterns and neural network inference requirements.
2Reliability
If redundant rows or columns are activated during neural read operations, then fault tolerance is improved, but decoding complexity increases
Solution Approach 1:
The decoding system is segmented into distinct decoding paths: one for conventional individual row/column access and another for neural read operations that activate all redundant rows/columns. This segmentation allows the system to handle different operation types with specialized decoding logic, managing complexity through functional separation.
Solution Approach 2:
The system performs preliminary identification of faulty rows or columns during manufacturing testing, storing this information in a redundancy map. During neural read operations, this pre-stored information is used to automatically activate the appropriate redundant rows or columns without requiring complex real-time decision-making, thereby reducing operational decoding complexity.
3Reliability
If multiple faulty rows or columns occur, then system reliability decreases, but the need for comprehensive redundancy increases
Solution Approach 1:
The system creates redundant copies of rows or columns during manufacturing testing and stores their addresses in a redundancy map. When faulty rows or columns are identified, pre-configured redundant copies are activated to replace them. This copying approach allows the system to handle multiple faults by having prepared backup resources available for immediate activation.
Solution Approach 2:
The decoding system uses feedback from the redundancy map (populated during manufacturing testing) to automatically determine which redundant rows or columns to activate when faults are detected. This feedback mechanism enables the system to adapt to multiple faulty elements by referencing pre-stored redundancy information, simplifying the management of comprehensive redundancy.
Data Source
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AI summary
Numerous embodiments are disclosed for accessing redundant non-volatile memory cells in place of one or more rows or columns containing one or more faulty non-volatile memory cells during a program, erase, read, or neural read operation in an analog neural memory system used in a deep learning artificial neural network.