NEON-optimized empirically-tuned Moog filter with hybrid processing. More...

#include "MoogFilter.h"
#include <arm_neon.h>

Classes
class	NeonMoogFilter
	Enhanced MoogFilter with ARM NEON SIMD optimization capabilities. More...

Detailed Description

NEON-optimized empirically-tuned Moog filter with hybrid processing.

ARM NEON optimized Moog filter with hybrid scalar/vector processing.

(Inferred)

This implementation combines the empirically-tuned Virtual Analog approach with ARM NEON SIMD optimization techniques, providing both single-channel scalar processing and multichannel vectorized processing modes. The design demonstrates practical optimization strategies for real-world audio processing applications.

@hybrid_processing_strategy The implementation provides multiple processing modes for different scenarios:

Scalar Mode: Optimized single-channel processing

Traditional sample-by-sample processing
Fast tanh approximation for nonlinear saturation
Empirically-tuned coefficients for musical character
Compatible with existing single-channel applications

SIMD Block Mode: Vectorized batch processing

Four-sample parallel processing using ARM NEON
Optimized for audio buffer processing
Maintains state continuity across vector boundaries
Hybrid scalar/vector approach for practical implementation

Adaptive Processing: Runtime selection of optimal mode

Automatic selection based on buffer size and alignment
Seamless fallback between vectorized and scalar processing
Optimal performance across diverse application scenarios

@neon_implementation_approach The NEON optimization focuses on practical real-world scenarios:

Block Processing Optimization: Vectorized buffer processing State Management: Careful handling of filter state across vector lanes Memory Alignment: Optimized for typical audio buffer layouts Hybrid Architecture: Combines best aspects of scalar and vector approaches Practical Efficiency: Designed for actual audio processing workflows

@performance_targeting

Live audio processing with minimal latency overhead
DAW plugin processing with efficient buffer handling
Mobile audio applications requiring power efficiency
Embedded systems with ARM processors and NEON capability
Real-time effects processing in resource-constrained environments

This header defines an advanced implementation that combines empirically-tuned Virtual Analog modeling with sophisticated ARM NEON SIMD optimization techniques. The implementation provides multiple processing modes including traditional scalar processing, optimized block processing, and advanced vectorized SIMD processing for maximum flexibility across diverse application scenarios.

@hybrid_optimization_strategy The implementation employs a sophisticated multi-tier optimization approach:

Adaptive Processing Selection: Runtime determination of optimal processing mode based on buffer size, data alignment, and computational requirements, ensuring maximum efficiency across diverse usage scenarios.

Scalar Foundation: Traditional sample-by-sample processing with fast tanh approximation optimized for single-channel applications and educational clarity.

Block Processing: Optimized batch processing for audio buffers using scalar operations with improved cache utilization and reduced function call overhead.

SIMD Vectorization: Advanced ARM NEON implementation processing four samples simultaneously with comprehensive vector optimization including custom division and nonlinear function approximations.

@arm_neon_optimization_techniques Vector Arithmetic: Complete vectorization of filter calculations using ARM NEON float32x4_t operations for parallel processing of four audio samples.

Custom Vector Division: Newton-Raphson approximation implementation for efficient vector division essential for rational function tanh approximation.

Vectorized Nonlinearity: SIMD implementation of fast tanh approximation providing parallel nonlinear processing with maintained accuracy characteristics.

Memory Optimization: Aligned data access patterns and optimized vector load/store operations for maximum memory bandwidth utilization.

@performance_characteristics

Scalar Mode: ~25 operations/sample, optimized for single-channel processing
Block Mode: ~20% improvement through reduced overhead and cache optimization
SIMD Mode: 2.5-3.5x speedup for aligned four-sample processing
Adaptive Selection: Automatic optimization based on runtime conditions
Memory Efficiency: Improved bandwidth utilization through vector operations

@applications

High-performance DAW plugins requiring adaptive optimization
Mobile audio applications needing power efficiency with performance
Real-time effects systems with variable processing requirements
Educational platforms demonstrating SIMD optimization techniques
Research applications analyzing vectorization effectiveness

Classes

Detailed Description