Adaptive Quantization for Fixed-Point Signal Processing
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Solution Overview
Problem
Current signal processing in disk-based storage devices experiences significant performance loss between fixed-point and floating-point implementations due to suboptimal quantization levels, leading to inefficiencies in data detection and decoding.
Innovation Solution
Implementing a fixed-point to floating-point optimization scheme with index and pattern-dependent quantization to reduce performance loss, where detectors and decoders use conditional distributions to determine optimal quantization levels for maximum information retention and error minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed-point implementation is used for signal processing, then device complexity and processing speed are improved, but measurement precision and manufacturing precision deteriorate due to suboptimal quantization levels
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization levels based on signal characteristics. The system transitions from fixed quantization levels to adaptive quantization levels that change according to the input signal's probability distribution, thereby maintaining detection accuracy while using fixed-point arithmetic.
Solution Approach 2:
The patent implements dynamics by making quantization levels adaptive rather than static. The quantization levels are adjusted in real-time based on the estimated probability distribution of the input signal, allowing the system to optimize its performance for different signal conditions while maintaining fixed-point implementation.
2Productivity
If fixed-point implementation is used for signal processing, then processing speed is improved, but manufacturing precision deteriorates due to quantization errors
Solution Approach 1:
The patent changes the quantization parameter from fixed to adaptive levels. By adjusting the quantization levels according to the signal's probability distribution, the system minimizes quantization errors and maintains processing precision while benefiting from the speed advantages of fixed-point arithmetic.
Solution Approach 2:
The patent applies preliminary action by estimating the probability distribution of the input signal before performing the actual signal processing. This pre-estimation allows the system to select optimal quantization levels in advance, thereby minimizing quantization errors before they occur during the main processing operation.
3Device complexity
If suboptimal quantization levels are used in fixed-point processing, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The patent changes the quantization parameter from uniform to non-uniform levels that are optimized for the signal's probability distribution. This parameter change allows the system to retain more information by allocating more quantization levels to regions of higher probability, thereby reducing information loss while maintaining fixed-point implementation.
Solution Approach 2:
The patent applies local quality by using different quantization levels for different regions of the signal range. Instead of using uniform quantization levels throughout, the system adjusts the quantization levels locally based on the signal's probability distribution, allocating finer resolution to high-probability regions and coarser resolution to low-probability regions.
4Measurement precision
If optimal quantization levels are determined using floating-point processing, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by performing floating-point probability distribution estimation only once before the main fixed-point signal processing. This preliminary floating-point operation determines the optimal quantization levels, which are then used throughout the subsequent fixed-point processing, thereby achieving high precision without the continuous overhead of floating-point arithmetic.
Solution Approach 2:
The patent uses an intermediary approach by introducing a probability distribution estimator that bridges the gap between floating-point optimization and fixed-point implementation. The estimator uses floating-point arithmetic to determine optimal quantization levels, which are then applied in the fixed-point processing stage, acting as an intermediary that enables precision optimization without continuous floating-point complexity.
Data Source
AI summary
An apparatus comprises read channel circuitry and signal processing circuitry associated with the read channel circuitry. The signal processing circuitry comprises a detector and a decoder coupled to the detector. The detector is configured to perform fixed-point detection on a digital data signal using a first set of quantization levels determined based at least in part on a result of a floating-point detection of the digital data signal. The decoder is configured to perform fixed-point decoding on an output of the detector using a second set of quantization levels determined based at least in part on a result of a floating-point decoding of the output of the detector.