Memory Controller Page Hit Prediction
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Solution Overview
Problem
Current memory access systems face inefficiencies due to high latency and power consumption, particularly when accessing system memory, as they struggle to manage complex DRAM transactions and maintain optimal row buffer usage across multiple banks, leading to reduced performance and increased power usage when locality is lost.
Innovation Solution
A memory controller that maintains access histories for each bank to predict page hits and set idle cycle limits based on page hit rates, allowing for more efficient management of row buffers by determining when to close open pages, thereby optimizing memory access and reducing latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If DRAM is organized into independent banks with pipelining to reduce latency, then memory access speed is improved, but device complexity increases
Solution Approach 1:
The DRAM memory device is divided into multiple independent banks, each capable of concurrent operations. This segmentation allows parallel memory access operations to proceed simultaneously across different banks, reducing overall access latency while managing complexity through modular organization where each bank operates independently with its own row buffers and control logic.
Solution Approach 2:
The system performs preliminary actions by precharging row buffers before actual data access operations. When a row is activated, its buffer is precharged to ready state, enabling immediate data retrieval without waiting for buffer preparation during the access operation itself. This preliminary buffering action reduces effective access latency.
2Speed
If row buffers are kept open to improve access speed, then memory access latency is reduced, but power consumption increases
Solution Approach 1:
The row buffer open/close state is made dynamic rather than static. The system continuously monitors access patterns and adjusts buffer states accordingly - keeping buffers open when access probability is high and closing them when access probability drops below a threshold. This dynamic state management optimizes the trade-off between access speed and power consumption based on actual workload characteristics.
Solution Approach 2:
The system implements feedback mechanisms by monitoring access patterns to determine when to close row buffers. Access histories and statistics are collected and fed back to the control logic, which adjusts buffer management decisions based on observed behavior. This feedback loop enables data-driven optimization of the balance between keeping buffers open for speed versus closing them to save power.
Data Source
AI summary
Systems, apparatuses, and methods for performing efficient memory accesses for a computing system are disclosed. In various embodiments, a computing system includes one or more computing resources and a memory controller coupled to a memory device. The memory controller determines a memory access request targets a given bank of multiple banks. An access history is updated for the given bank based on whether the memory access request hits on an open page within the given bank and a page hit rate for the given bank is determined. The memory controller sets an idle cycle limit based on the page hit rate. The idle cycle limit is a maximum amount of time the given bank will be held open before closing the given bank while the bank is idle. The idle cycle limit is based at least in part on a page hit rate for the bank.