Graph Cache Reconfiguration Using CAM-RAM Neighbor Lookup
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Solution Overview
Problem
Graph neural networks (GNNs) face inefficiencies due to the large memory requirements of sparse adjacency matrices used to represent graph structures, leading to suboptimal memory utilization and processing inefficiencies, especially for large graphs.
Innovation Solution
A graph cache architecture utilizing a content addressable memory (CAM) array and random access memory (RAM) array is employed to store and retrieve graph structures efficiently, allowing for rapid lookup and reconfiguration of graph neural network processing without the need for sparse adjacency matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sparse adjacency matrices are used to represent graph structures, then graph neural networks can process and analyze graph data, but memory requirements increase and memory utilization becomes suboptimal
Solution Approach 1:
The patent segments the graph structure representation from the traditional sparse adjacency matrix into a graph cache system that separates node identifiers (stored in CAM array) from their corresponding data (stored in RAM array). This segmentation allows efficient lookup of neighbor nodes without storing the entire sparse matrix, reducing memory requirements while maintaining processing efficiency.
Solution Approach 2:
The patent introduces a graph cache as an intermediary component between the CPU and the graph data storage. This graph cache includes a content addressable memory (CAM) array and a random access memory (RAM) array, which work together to provide fast lookup of graph structures without requiring the CPU to access large sparse adjacency matrices directly, thus improving processing efficiency while reducing overall memory requirements.
2Quantity of substance
If sparse adjacency matrices are used to represent graph structures, then graph neural networks can operate with complete graph information, but memory utilization becomes suboptimal
Solution Approach 1:
Instead of storing the traditional sparse adjacency matrix where rows and columns represent node connections, the patent inverts the approach by using a graph cache where the CAM array stores node identifiers and the RAM array stores corresponding data. This inversion allows direct addressable access to graph data through node identifiers, improving memory utilization while managing data structure complexity through the use of specialized memory hardware.
3Speed
If traditional memory access methods are used for graph data, then data can be stored in standard memory, but lookup operations become inefficient for finding neighbor embeddings
Solution Approach 1:
The graph cache architecture serves multiple functions: the CAM array provides fast content-addressable lookup of node identifiers, while the RAM array stores and provides access to the actual graph data and embeddings. This multi-functional design enables both fast lookup operations and efficient data storage within a single integrated memory system, improving lookup speed while managing architectural complexity through functional specialization.
Solution Approach 2:
The patent replaces the traditional mechanical/random access memory lookup mechanism with a content addressable memory (CAM) system that performs parallel comparison of search keys against stored identifiers. This substitution of the lookup mechanism enables constant-time or near-constant-time lookup operations regardless of graph size, dramatically improving lookup speed while the specialized CAM hardware manages the added architectural complexity.
Data Source
AI summary
A graph cache may be dynamically reconfigured to adapt to changes in a graph or access requirements. In an implementation, the graph cache includes a content addressable memory (CAM) array comprising CAM rows and match lines, configured to receive an identifier of a target node of a graph structure and a key associated with a user credential, search for a combination of the identifier and key, and activate match lines corresponding to matching CAM rows. A random-access memory (RAM) array comprises RAM rows and word lines. A multiple match resolver serially activates word lines of the RAM array corresponding to activated match lines. A management processor receives an encrypted input, decrypts it to obtain the identifier and key, and provides the combination to the CAM array.