Data Management Device for ANN Latency Reduction
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Solution Overview
Problem
The performance of artificial neural network operations is hindered by the imbalance between processor and memory performance, particularly in data-intensive processing, leading to degraded overall operation performance due to high latency and bandwidth limitations in memory devices like DRAM and NAND flash.
Innovation Solution
A data management device and method that predicts memory addresses for upcoming artificial neural network operations based on the type and characteristics of the network, using a processor interface circuit, memory interface circuit, and internal controller to optimize data caching and reduce latency by pre-fetching data, thereby enhancing bandwidth utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data caching based on data locality is implemented, then operation speed is improved, but device complexity increases
Solution Approach 1:
The patent implements data prefetching by predicting future data access patterns based on neural network operation characteristics. The controller pre-loads data into cache memory before it is actually needed by the processor, eliminating wait time and improving operation speed without requiring complex real-time data management
Solution Approach 2:
The system employs data locality awareness where the controller automatically identifies and caches data that will be needed based on the inherent access patterns of neural network operations. This self-organizing cache management improves speed without requiring external intervention or complex control mechanisms
2Loss of time
If in-memory processing is implemented, then data input/output time is reduced, but adaptability to various neural network types is limited
Solution Approach 1:
The patent designs a universal data management controller that can handle multiple neural network architectures (CNN, RNN, Transformer, etc.) through a single unified interface. The controller adapts to different network types by recognizing their specific data access patterns and applying appropriate caching strategies, maintaining both speed and versatility
Solution Approach 2:
The system dynamically adjusts its data management strategy based on the type of neural network operation being performed. The controller can switch between different prefetching patterns, cache sizes, and data organization methods to optimize performance for specific network architectures while maintaining a unified in-memory processing approach
Data Source
AI summary
Disclosed is a data cache or data management device for caching data between at least one processor and at least one memory, and supporting an artificial neural network (ANN) operation executed by the at least one processor. The data cache device or the data management device can comprise an internal controller for predicting the next data operation request on the basis of ANN data locality of the ANN operation. The internal controller monitors data operation requests associated with the ANN operation from among data operation requests actually made between the at least one processor and the at least one memory, thereby extracting the ANN data locality of the ANN operation.