Sequence-Guided DMA Memory Controller for Neural Network Latency
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Solution Overview
Problem
Conventional artificial neural network models face issues with high-power consumption, heating, and bottlenecks due to low memory bandwidth and latency, leading to degraded operation performance and inefficient data processing.
Innovation Solution
An artificial neural network memory system that utilizes data locality information to predict and prepare data access requests in advance, minimizing memory latency and bandwidth issues by optimizing memory operations at the processor-memory level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional memory access methods are used in artificial neural network processing, then the system structure is simple, but memory bandwidth is low and memory latency is high causing processing bottlenecks
Solution Approach 1:
The patent implements a memory access prediction mechanism that analyzes historical access patterns and pre-loads data into buffer memory before it is actually needed by the processor. This preliminary action eliminates waiting time for memory operations, directly reducing memory latency and improving processing throughput in neural network workloads.
Solution Approach 2:
The patent introduces a buffer memory as an intermediary layer between the main memory and the processor. This buffer memory stores frequently accessed data and prediction results, acting as a mediator that reduces the direct access burden on main memory and accelerates data delivery to the processor, thereby improving memory bandwidth utilization.
2Productivity
If conventional memory access methods are used in artificial neural network processing, then the system structure is simple, but memory bandwidth is low causing processing bottlenecks
Solution Approach 1:
The memory access prediction mechanism pre-loads data into buffer memory based on predicted access patterns, ensuring that data is ready before the processor needs it. This increases the effective memory bandwidth by reducing idle cycles and ensuring continuous data flow to the processor during neural network operations.
Solution Approach 2:
The buffer memory serves as an intermediary that decouples the processor from main memory constraints. By caching prediction results and frequently accessed data in the buffer, the system effectively increases the available bandwidth to the processor without requiring increases in main memory bandwidth capacity.
3Productivity
If data is not prepared in advance in memory, then memory operations are simple, but data supply delay occurs frequently degrading operation performance
Solution Approach 1:
The patent implements a prediction mechanism that identifies future data access needs and pre-loads corresponding data into buffer memory before the processor actually requests it. This preliminary data preparation eliminates supply delays and ensures continuous operation, directly improving neural network processing performance.
Solution Approach 2:
The system employs a feedback mechanism where the prediction mechanism continuously monitors actual access patterns and adjusts its predictions accordingly. This feedback loop ensures that data is prepared in advance with high accuracy, minimizing mismatches between predicted and actual access patterns and maintaining optimal performance.
Data Source
AI summary
According to an example of the present disclosure, a system is provided. A system may include a main memory including a dynamic memory cell electrically coupled to a bitline and a word line, and a memory controller configured to selectively omit a restore operation during a read operation of the dynamic memory cell.