ML Memory Prefetch Model Using Page Fault Virtual Addresses
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Solution Overview
Problem
Conventional prefetch algorithms struggle to predict complex memory access patterns across page boundaries, especially when dealing with a large number of unique address values, leading to inefficiencies in caching data in faster memory.
Innovation Solution
The use of a Machine Learning (ML) based prefetch model that incorporates page fault information, including virtual addresses, to improve the prediction of data access patterns and optimize caching in a memory device with a slower access latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Machine Learning prefetch algorithms are used to predict data access patterns, then prediction accuracy may improve, but the algorithms become inefficient at predicting complex access patterns across page boundaries
Solution Approach 1:
The patent introduces a page table as an intermediary structure between virtual addresses and physical addresses. The page table is used to translate virtual addresses to physical addresses, which enables the prefetch algorithm to understand the relationship between accessed pages and predict future accesses more accurately. This intermediary structure resolves the inefficiency of direct physical address prediction by providing a layered abstraction that simplifies the prediction problem.
Solution Approach 2:
The patent shifts the prediction approach from operating directly on physical addresses to operating on virtual addresses and page tables. This dimensional change allows the algorithm to work with a more manageable representation of memory access patterns. By using virtual addresses and page table entries, the algorithm can predict access patterns across page boundaries more effectively, as the page table structure provides natural groupings that simplify the prediction task.
2Adaptability or versatility
If the Operating System assigns contiguous virtual pages to seemingly random physical pages, then memory allocation flexibility is improved, but prefetch algorithms struggle to predict access patterns
Solution Approach 1:
The page table serves as an intermediary that preserves the random physical page assignment while enabling predictable access pattern recognition. By translating virtual addresses to physical addresses through the page table, the system maintains the flexibility of random physical allocation while providing the prediction algorithm with the structured information needed to identify access patterns across page boundaries.
Solution Approach 2:
The patent segments the memory address space into virtual pages and physical pages through the page table structure. This segmentation allows the system to maintain flexible physical page assignment while creating a structured representation of memory access patterns at the virtual page level. The segmentation enables the prefetch algorithm to operate on page boundaries and predict access patterns without being constrained by the random physical page mapping.
3Device complexity
If conventional incrementing prefetch algorithms are used, then implementation simplicity is maintained, but performance deteriorates on complex access patterns
Solution Approach 1:
The page table acts as an intermediary that enables more sophisticated prefetch algorithms to operate efficiently. Instead of directly implementing complex prediction logic on physical addresses, the algorithm uses the page table to translate and analyze virtual address patterns, which simplifies the implementation while improving performance on complex access patterns.
Solution Approach 2:
The patent changes the parameters used for prefetch prediction from physical addresses to virtual addresses and page table entries. This parameter change allows the algorithm to leverage the structured information in the page table to predict access patterns more accurately, while the overall implementation remains relatively simple by building upon existing page table traversal mechanisms.
Data Source
AI summary
A memory device includes a first memory and a second memory that caches data stored in the first memory. At least one controller of the memory device receives page fault information from a host. The page fault information results from a request for data by the host that is stored in the first memory but is not cached in the second memory when requested by the host. The memory device uses the received page fault information for one or more inputs into a prefetch model trained by Machine Learning (ML) to generate at least one inference. Based at least in part on the at least one inference, prefetch data is cached in the second memory. In one aspect, the page fault information is used to train the prefetch model. In another aspect, the page fault information includes at least one virtual address used by the host for the requested data.