State-Split Encoder-Decoder Graphs for Constrained Data Channels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Designing read channel devices to encode and decode data for constrained systems, such as hard disk drives, is challenging due to the need to avoid complex circuitry while ensuring accurate data retrieval, particularly in reducing long runs of consecutive transitions or ones, which existing technologies struggle to achieve efficiently.
Innovation Solution
The implementation of state-split based encoders and decoders that generate a directed graph to reduce hardware complexity by simplifying the memory structure, resulting in a final digraph with fewer branches per state, thereby easing the design of encoders and decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encoder and decoder circuits are designed to handle constrained systems, then data encoding and decoding can be achieved, but hardware complexity increases due to the need to track multiple branches in the directed graph
Solution Approach 1:
The patent applies segmentation by dividing the complex directed graph into multiple simplified sub-graphs, where each sub-graph represents a specific constraint condition. The encoder and decoder are designed to operate on these segmented sub-graphs rather than the complete complex graph, reducing hardware complexity while maintaining data integrity through systematic decomposition of the encoding/decoding task across multiple simpler computational paths
2Device complexity
If the directed graph is simplified to reduce hardware complexity, then encoder and decoder design becomes easier, but the ability to accurately represent constraint sets may be compromised
Solution Approach 1:
The patent employs dynamics by making the encoder and decoder adaptive to different constraint conditions. The system dynamically selects and switches between different simplified sub-graph representations based on the specific constraint set being applied, allowing the hardware to maintain accuracy for various constraint types without requiring a single complex fixed structure. This dynamic adaptation enables precise constraint representation while keeping individual encoder/decoder implementations simple
3Device complexity
If memory structure is simplified to reduce hardware complexity, then state tracking becomes easier, but the precision of tracking state changes across branches may be reduced
Solution Approach 1:
The patent introduces intermediary computational elements that act as mediators between the simplified memory structure and the constraint satisfaction requirements. These intermediaries process and transform state information in a way that allows accurate tracking of constraint compliance using simpler memory structures. The intermediaries effectively bridge the gap between reduced memory complexity and the need for precise state tracking across what would traditionally require complex multi-branch tracking
Data Source
AI summary
A method of generating a hardware encoder includes generating a first directed graph characterizing a constraint set for a constrained system, identifying a scaling factor for an approximate eigenvector for the first directed graph, applying the scaling factor to the approximate eigenvector for the first directed graph to yield a scaled approximate eigenvector, partitioning arcs between each pair of states in the first directed graph, performing a state splitting operation on the first directed graph according to the partitioning of the arcs to yield a second directed graph, and generating the hardware encoder based on the second directed graph.