Inter-Processor Codebook Encoding for Fast Secure Chip Communication
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Solution Overview
Problem
The rapid growth in data storage demand, exceeding available physical capacity and transmission bandwidth, coupled with increasing processing power needs and security concerns, particularly in complex chip environments, necessitates a new approach for efficient data storage and transmission between processors.
Innovation Solution
The implementation of AI-driven encoding techniques that use machine learning to identify patterns in data, reducing the number of bits needed for transmission by storing and transferring smaller codewords, which are instantly decoded at the destination, thereby enhancing data compaction and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to reduce storage capacity requirements, then storage capacity is improved, but transmission bandwidth and processing time are worsened due to compression overhead
Solution Approach 1:
The patent applies preliminary action by pre-training the encoding model offline to learn data patterns and create a codebook, then using this pre-trained model for rapid online encoding. The computationally intensive pattern recognition is performed beforehand, allowing real-time encoding to only require simple dictionary lookups and codeword generation, thus reducing processing time while maintaining compression efficiency
Solution Approach 2:
The patent extracts only the essential pattern information from the training data and stores it as a compact codebook containing codewords and frequency information. Instead of storing or transmitting the entire training dataset or complex model parameters, only the extracted codeword mappings are retained, enabling fast encoding without the overhead of processing the full original data during compression
2Productivity
If more processors are added to increase processing power, then processing capability is improved, but data transmission cost and bandwidth requirements are worsened
Solution Approach 1:
The patent uses copying by creating a shared codebook that is replicated across all processors in the distributed system. Each processor has access to the same codeword mappings, allowing them to independently encode data without requiring complex coordination or communication about encoding schemes. This shared reference enables efficient inter-processor communication through compact codewords
Solution Approach 2:
The patent transforms the data representation parameters by converting original data into a different parameter space using codewords. Instead of transmitting raw data values, the system transmits compact codeword indices that reference the shared codebook, fundamentally changing how data is represented and transmitted between processors to reduce bandwidth requirements
3Quantity of substance
If traditional compression algorithms are used to reduce data size, then data size is reduced, but transmission speed and latency are worsened
Solution Approach 1:
The patent performs the computationally intensive pattern learning and codebook generation in advance during an offline training phase. The pre-trained codebook captures all necessary compression knowledge, allowing online encoding to proceed with simple, fast dictionary-based lookups rather than complex algorithmic processing, thus achieving both small data size and high transmission speed
Solution Approach 2:
The patent replaces traditional mechanical compression algorithms with a dictionary-based coding system. Instead of using complex mathematical transformations and processing during compression, the system substitutes a pre-computed lookup table approach where encoding becomes a simple matter of finding and copying pre-defined codewords, dramatically reducing computational overhead and increasing transmission speed
Data Source
AI summary
A system and method for fast communication between processors on complex chips using encoding, wherein a training data set is used to find patterns and associated smaller indices, or codewords, which are stored in a reference codebook library, and where reconstruction and deconstruction algorithms are used to encode and decode data as it is received. The codebook and algorithms may be stored in the firmware of a semiconductor which enable reduced resources and cost when transmitting data between or among devices that utilize such semiconductors.