Memory Controller Hamming Distance Processing for Neural Network Efficiency
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Solution Overview
Problem
Deep learning neural networks face challenges in processing large datasets, particularly in memory-intensive environments like image processing, due to high memory and data transfer requirements, which can lead to inefficiencies and delays in processing and comparison of image codes.
Innovation Solution
Implementing a system where Hamming image codes are generated and processed at edge devices, such as IoT devices, using memory devices to compare image codes directly, reducing the need for extensive memory requests and enabling faster processing by distributing calculations closer to memory devices through Hamming processing units and memory controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If image codes are processed and compared using traditional memory systems, then processing accuracy can be maintained, but processing speed decreases and memory bandwidth requirements increase
Solution Approach 1:
The patent segments the image code comparison task into parallel operations performed by multiple processing elements within the memory device. Each processing element handles a portion of the Hamming distance calculations simultaneously, dividing the overall processing load and enabling faster completion without requiring proportionally more memory bandwidth.
Solution Approach 2:
The patent merges the image code processing functionality directly into the memory device, combining storage and processing operations into a single integrated system. This eliminates the need to transfer data between separate memory and processing units, reducing memory bandwidth requirements while maintaining processing speed.
2Reliability
If large datasets are stored and processed in neural networks, then model accuracy and comprehensiveness improve, but memory requirements and data transfer overhead increase
Solution Approach 1:
The patent introduces an intermediary processing layer within the memory device that performs Hamming distance calculations on image codes before data needs to be transferred to external processors. This intermediary processing reduces the volume of data that requires high-bandwidth transfer, lowering energy consumption while preserving the ability to work with large datasets for model accuracy.
3Productivity
If Hamming distance calculations are performed using conventional processors, then flexibility in processing different algorithms is maintained, but processing latency increases
Solution Approach 1:
The patent replaces conventional general-purpose processor operations with specialized hardware circuits within the memory device that are optimized for Hamming distance calculations. This substitution of mechanical/software-based processing with dedicated hardware logic significantly reduces processing latency while maintaining high throughput for image code comparisons.
Data Source
AI summary
Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory controller with various memory devices. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing request, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed “closer” to the memory devices, e.g., at the memory controller coupled to memory devices.