Row Hammer Mitigation Using Content Addressable Memory
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Solution Overview
Problem
Conventional row hammer detection techniques in memory devices are either impractical due to high resource requirements or imperfect, leading to potential data corruption and performance degradation, especially in hyperscale datacenters where malicious attacks can compromise system security.
Innovation Solution
A memory controller with a row hammer mitigation component that uses a space-saving algorithm and content addressable memory (CAM) to maintain a limited number of counters, allowing for perfect tracking of row hammer attacks without the need for a separate counter for each row, thereby reducing resource usage and ensuring accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional row hammer detection techniques use a separate counter for each row, then detection accuracy is improved, but device complexity and resource usage increase significantly
Solution Approach 1:
The patent merges multiple row counters into a single shared counter resource. Instead of allocating separate counters to each memory row, the system uses one counter that is dynamically allocated to different rows through a mapping mechanism, thereby reducing device complexity while maintaining detection accuracy.
Solution Approach 2:
The counter is designed to serve multiple rows universally. The same counter infrastructure is reused across different rows through a row-to-counter mapping table, allowing a single counter to perform the function of multiple dedicated counters and reducing overall system complexity.
2Reliability
If conventional row hammer detection techniques allocate counters for all rows, then detection completeness is improved, but area usage increases excessively
Solution Approach 1:
The system implements partial monitoring by allocating counters only to rows that are currently active or potentially vulnerable to row hammer attacks. Instead of maintaining counters for all rows simultaneously, the system dynamically activates counters based on actual access patterns and vulnerability assessments.
Solution Approach 2:
The patent segments the counter resources into multiple groups or pools, where each group can be allocated to different rows at different times. This segmentation allows the system to manage counter allocation more efficiently and reduce the total area required while maintaining detection coverage.
3Measurement precision
If conventional row hammer detection uses frequent counter resets, then detection accuracy is improved, but productivity decreases due to performance overhead
Solution Approach 1:
The system implements periodic counter resets based on refresh intervals rather than continuous resetting. Counters are reset at predetermined periods aligned with memory refresh cycles, which maintains detection accuracy while minimizing the frequency of reset operations and their associated performance overhead.
Solution Approach 2:
The system performs preliminary identification of rows that require counter resets before actually executing the reset operation. By pre-identifying vulnerable rows and scheduling their counter resets in advance, the system can optimize the timing of reset operations to minimize interference with normal memory access and improve overall productivity.
Data Source
AI summary
Systems and methods for area-efficient mitigation of errors that are caused by row hammer attacks and the like in a memory media device are described. The counters for counting row accesses are maintained in a content addressable memory (CAM) the provides fast access times. The detection of errors is deterministically performed while maintaining a number of row access counters that is smaller than the total number of rows protected in the memory media device. The circuitry for the detection and mitigation may be in the memory media device or in a memory controller to which the memory media device attaches. The memory media device may be dynamic random access memory (DRAM).