18347318. COMPACT CAM ARRAY FOR DECISION TREE INFERENCE (TREE-CAM) simplified abstract (Hewlett Packard Enterprise Development LP)

COMPACT CAM ARRAY FOR DECISION TREE INFERENCE (TREE-CAM)

Organization Name

Hewlett Packard Enterprise Development LP

Inventor(s)

GIACOMO Pedretti of Cernusco sul Naviglio (MI) (IT)

Ron M. Roth of Palo Alto CA (US)

COMPACT CAM ARRAY FOR DECISION TREE INFERENCE (TREE-CAM) - A simplified explanation of the abstract

This abstract first appeared for US patent application 18347318 titled 'COMPACT CAM ARRAY FOR DECISION TREE INFERENCE (TREE-CAM)

Simplified Explanation: The technology disclosed in the patent application introduces "tree-CAMs" that are designed to efficiently implement decision trees with fewer hardware components, lower power consumption, and reduced programming time compared to existing aCAMs.

• The tree-CAMs leverage a shared comparison sub-circuit to store a threshold shared among multiple root-to-leaf paths of a decision tree.
• This shared comparison sub-circuit, which may consist of just a single memristor programmed to store the threshold, allows for the representation of evaluable conditions across multiple paths using a single memristor.
• By reducing the number of memristors required to implement the decision tree, the tree-CAMs offer a more efficient and optimized solution for decision tree implementation.

Key Features and Innovation:

• Introduction of "tree-CAMs" for efficient decision tree implementation.
• Utilization of a shared comparison sub-circuit to store a common threshold for multiple paths.
• Reduction in hardware components, power consumption, and programming time.
• Representation of evaluable conditions across multiple paths using a single memristor.

Potential Applications: The technology can be applied in various fields such as:

• Machine learning
• Artificial intelligence
• Pattern recognition
• Data analysis

Problems Solved:

• High hardware requirements for decision tree implementation.
• Excessive power consumption.
• Lengthy memristor programming time.

Benefits:

• Improved efficiency in decision tree implementation.
• Reduced hardware costs.
• Lower power consumption.
• Faster programming time.

Commercial Applications: Potential commercial uses include:

• Developing efficient machine learning algorithms.
• Enhancing AI systems.
• Improving data analysis tools.
• Optimizing pattern recognition software.

Prior Art: Readers can explore prior art related to decision tree implementation, memristor technology, and content-addressable memories.

Frequently Updated Research: Stay updated on the latest advancements in memristor technology, machine learning algorithms, and artificial intelligence systems.

Questions about Decision Tree Implementation: 1. How does the shared comparison sub-circuit in tree-CAMs optimize decision tree implementation? 2. What are the potential implications of reducing hardware components in decision tree implementation?

Original Abstract Submitted

Examples of the presently disclosed technology provide “tree-CAMs” specially constructed to implement decision trees more efficiently—namely with less hardware (i.e., fewer memristors and transistors), less power consumption, and less memristor programming time than existing aCAMs used to implement decision trees. A tree-CAM realizes these optimizations by leveraging a shared comparison sub-circuit that stores a threshold shared among evaluable conditions for multiple root-to-leaf-paths of a decision tree. The threshold may be evaluated against a common feature of a feature vector. The shared comparison sub-circuit may comprise just a single memristor programmed to store the threshold. Accordingly, the tree-CAM can represent/implement evaluable conditions (sharing a common threshold and evaluated against a common feature) across multiple root-to-leaf paths of a decision tree using just a single memristor—thus reducing the number of memristors required to implement the decision tree.