ANN Memory Controller Using Data Locality to Cut 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 processor starvation.
Innovation Solution
An artificial neural network memory system that utilizes artificial neural network data locality to optimize memory operations by predicting and preparing data access requests in advance, minimizing latency and bandwidth issues through an artificial neural network memory controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If memory operations are performed using conventional methods without data locality optimization, then memory bandwidth and latency are insufficient, but power consumption increases and processing performance degrades
Solution Approach 1:
The memory controller predicts future data access requests by analyzing data locality patterns and pre-fetches data into the buffer memory before the processor actually requests it. This preliminary action eliminates waiting time and ensures data is ready when needed, improving processing performance without increasing power consumption of the processor.
Solution Approach 2:
A buffer memory is introduced as an intermediary between the main memory and the processor. The memory controller manages this buffer to store frequently accessed or predicted data, reducing the frequency of main memory accesses and thereby improving performance while managing power consumption through selective buffering.
2Productivity
If memory bandwidth is increased to handle large artificial neural network models, then processing performance improves, but memory latency increases and power consumption rises
Solution Approach 1:
The system performs preliminary data fetching by predicting future access patterns based on data locality. Data is loaded into the buffer memory in advance, so when the processor needs the data, it is already available, effectively eliminating memory latency without requiring sustained high memory bandwidth.
Solution Approach 2:
The buffer memory is used to store only the specific data that is predicted to be accessed soon, based on data locality analysis. This localized caching of relevant data ensures fast access for critical operations without the overhead of maintaining high bandwidth for all memory operations.
3Ease of operation
If data is fetched from memory in real-time without prediction, then memory control is simple, but processor starvation occurs and operation performance degrades
Solution Approach 1:
The memory controller implements a feedback mechanism by monitoring actual data access patterns and using this information to refine its predictions of future access requests. This feedback loop enables the controller to anticipate processor needs more accurately, preventing processor starvation while maintaining relatively simple control logic based on observed patterns.
Solution Approach 2:
The system enables the memory controller to serve itself by automatically analyzing access patterns and making prediction decisions without requiring complex external control. The controller uses the data locality information it already has to autonomously determine what data to pre-fetch, simplifying the overall control architecture while improving performance.
Data Source
AI summary
According to an example of the present disclosure, a system is provided. A system may include a processor configured to output a memory control signal including an artificial neural network data locality, and a memory controller configured to receive the memory control signal from the processor and control a main memory in which data of an artificial neural network model corresponding to the artificial neural network data locality, is stored.