Hierarchical Codeword Caching for Low-Latency ML Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep learning systems face challenges in efficiently processing large volumes of data while maintaining privacy and security, particularly in sensitive domains like healthcare and finance, where secure collaborations across entities are needed.
Innovation Solution
A hierarchical smart caching system for machine learning codeword responses that integrates homomorphic compression, dyadic encryption, and large codeword models, utilizing a dual-tier caching architecture with local and global caches, and advanced machine learning cores to enhance computational efficiency and response quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large codeword models are used to process data while maintaining privacy and security, then data processing capability and security are improved, but computational requirements and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by caching processed codeword responses before they are needed. The hierarchical cache stores previously generated responses at multiple levels (device cache, edge cache, cloud cache), so when similar queries occur, the system can retrieve cached results instead of reprocessing data through the large codeword model. This preliminary caching action significantly reduces computational resource consumption while maintaining security and privacy protections.
Solution Approach 2:
The patent implements local quality by creating a hierarchical caching structure with different levels of storage capacity and access speed tailored to specific needs. Device-level caches provide fast local access for frequent queries, while edge and cloud caches provide larger storage capacity for less frequently accessed but still relevant responses. This localized caching strategy optimizes resource usage by keeping frequently needed data locally rather than requiring repeated cloud processing.
2Measurement precision
If computational processing is performed for every user prompt, then response accuracy is maintained, but processing time and system latency increase
Solution Approach 1:
The system creates copies of previously generated codeword responses and stores them in a hierarchical caching structure. When a user prompt matches or closely resembles a previously processed query, the system retrieves the cached copy instead of regenerating the response. This copying approach maintains response accuracy by reusing proven correct outputs while dramatically reducing processing time and system latency.
Solution Approach 2:
The system performs preliminary processing and caching of responses during idle or low-load periods, preparing results in advance for future queries. By anticipating likely user questions and pre-computing responses, the system reduces latency when users actually submit queries, while maintaining accuracy through the use of pre-validated cached responses.
3Adaptability or versatility
If a distributed caching system is implemented across multiple devices, then system scalability and knowledge sharing are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the caching system into distinct hierarchical levels: device-level caches on individual user devices, edge caches at intermediate servers, and cloud caches at central infrastructure. Each segment operates semi-independently with clear responsibility boundaries. This segmentation enables scalability by allowing each level to grow independently while maintaining simple coordination protocols between levels, reducing overall system complexity despite the distributed architecture.
Solution Approach 2:
The system introduces intermediary components that mediate between different cache levels and the large codeword model. These intermediaries handle the complexity of coordination, validation, and synchronization, shielding individual devices from the full complexity of the distributed system. The intermediary layer manages knowledge sharing across devices while maintaining simple interfaces for individual components, thus improving scalability without proportionally increasing device complexity.
Data Source
AI summary
A system and method for deep learning using a large codeword model with hierarchical caching is disclosed. The system processes input prompts into tokens, maps them to codewords using a codebook, and processes these through a machine learning core to generate responses. A sophisticated caching architecture stores and retrieves responses across both local and global cache tiers. The local cache maintains frequently accessed responses on edge devices through short-term and persistent storage components, while the global cache enables knowledge sharing across multiple devices. A context aggregator identifies relationships between cached responses to form comprehensive contextual representations. This hierarchical caching system significantly reduces computational requirements by reusing previously generated responses for similar prompts, while continuously optimizing cache contents based on relevance scoring and usage patterns. The approach enables efficient scaling across distributed environments while maintaining response quality.