Hard Drive Queue Architecture for Partial De-Interleaving
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Solution Overview
Problem
Hard decision decoding in data read channels lacks efficient mechanisms for handling interleaved data and error correction, particularly in systems where partial de-interleaving and noise prediction are critical, leading to suboptimal performance in error detection and data processing.
Innovation Solution
The proposed Hard Decision Queue (HDQ) design performs local de-interleaving, supports error bit counting, and integrates with a noise-predictive calculation function, enabling partial de-interleaving and efficient data transfer across multiple clients, including a read/write interface, while optimizing bandwidth and latency through time division multiplexing and ping-pong buffers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hard decision decoding uses a simple threshold detector, then the device complexity is reduced, but the measurement precision of bit detection deteriorates
Solution Approach 1:
The patent segments the bit detection process into multiple confidence levels (definitely 0, probably 0, maybe 0, guess 0, guess 1, maybe 1, probably 1, definitely 1) using a multi-level threshold detector. This segmentation allows the system to maintain relatively simple detector architecture while achieving higher measurement precision through graduated confidence assessment rather than binary decisions.
Solution Approach 2:
The patent changes the detection parameter from binary (0 or 1) to multi-level confidence indicators (000-111 codes representing different confidence levels). This parameter transformation enables the threshold detector to provide soft decision information with higher precision while maintaining acceptable device complexity through systematic voltage level comparison.
2Productivity
If the HDQ implements partial de-interleaving for 4k sectors, then the productivity of data processing is improved, but the device complexity increases
Solution Approach 1:
The patent implements partial de-interleving for 4k sectors rather than complete de-interleving. The HDQ performs de-interleaving on a subset of data elements (4b per quarter clock period) which provides sufficient productivity improvement for the application while avoiding the full complexity burden of complete de-interleving architecture. This partial action achieves the necessary processing throughput with manageable complexity.
Solution Approach 2:
The patent segments the 4k sector data processing into multiple quarters, with each quarter handled separately by the HDQ. This segmentation allows the queue to manage complexity by processing smaller chunks of data (4b per ckq) rather than handling the entire 4k sector at once, thereby improving productivity through pipelining while keeping the per-stage complexity manageable.
3Adaptability or versatility
If the HDQ supports multiple clients with different data rates, then the adaptability of the system is improved, but the loss of time due to synchronization increases
Solution Approach 1:
The patent employs periodic action through time division multiplexing where the HDQ alternates service among multiple clients at regular intervals (quarter clock periods). Each client receives dedicated time slots for data transfer, allowing the system to support diverse data rates (4b/ckq for RWI and NPCAL, 48b/cko for LEH and LE LN) while managing synchronization through periodic scheduling rather than continuous arbitration.
Solution Approach 2:
The patent implements preliminary action by pre-configuring the HDQ with knowledge of client data rate requirements and establishing predetermined service patterns. The queue is designed beforehand to handle the specific bandwidth requirements of each client type, allowing efficient time-division multiplexing without real-time negotiation delays, thus reducing synchronization overhead while maintaining high adaptability.
Data Source
AI summary
Computer-implemented methods and systems may perform one or more operations including, but not limited to: receiving input data from a source; applying an interleaving protocol to the input data to generate at least one component codeword; decoding the at least one component codeword; determining a first convergence value of at least one decoded component codeword; computing extrinsic data associated with the at least one component codeword according to the bit error count and the first convergence of at least one component codeword; determining a second convergence value associated with the at least one component codeword according to the extrinsic data associated with the at least one component codeword.