05_16_04_precision_variants¶

Multi-Precision Audio Processing Variants¶

Status: ⏸️ NOT STARTED Priority: MEDIUM Dependencies: TAREA 0 (Variant Framework) Estimated Effort: 2 weeks

🎯 Purpose¶

Precision variants provide multiple floating-point precision options (float16, float32, float64, fixed-point) to optimize for different use cases: - float16 (half): Mobile GPUs, ML inference (2x memory savings) - float32 (single): Standard audio processing (current default) - float64 (double): High-precision scientific audio (e.g., synthesis) - Fixed-point: Embedded systems, DSPs

📋 Planned Variants¶

Float16 Variants (GPU/Mobile)¶

class Float16GainVariant : public IVariant {
public:
    bool process(const float* input, float* output, size_t numSamples) override;
    // Converts to fp16 internally, uses GPU fp16 ops

private:
    __half gain_;  // 16-bit
};

Use Cases: - Mobile devices (iOS/Android) - GPU processing (Tensor Cores) - ML audio models (inference)

Trade-offs: - 2x memory savings - 2x bandwidth savings - Lower precision (10-bit mantissa vs 23-bit)

Float64 Variants (High Precision)¶

class Float64OscillatorVariant : public IVariant {
public:
    bool process(const float* input, float* output, size_t numSamples) override;
    // Uses double internally, converts back to float

private:
    double phase_;
    double phaseIncrement_;
};

Use Cases: - Scientific audio analysis - Long-running synthesis (phase accumulation) - Cascaded filters (error accumulation prevention)

Trade-offs: - Higher precision - 2x memory usage - Slower (no SIMD double on some CPUs)

Fixed-Point Variants (Embedded)¶

class FixedPoint16GainVariant : public IVariant {
public:
    bool process(const float* input, float* output, size_t numSamples) override;
    // Fixed Q15 format internally

private:
    int16_t gain_;  // Q15: 1 sign bit, 0 integer bits, 15 fractional bits
};

Use Cases: - Embedded systems without FPU - DSP chips - Low-power devices

📊 Performance Targets¶

Variant	Memory Usage	Bandwidth	Speed	Precision
float16	0.5x	0.5x	1.5-2x (GPU)	Medium
float32	1x	1x	1x (baseline)	High
float64	2x	2x	0.5-0.8x	Very High
fixed16	0.5x	0.5x	1.2-1.5x (embedded)	Medium

🛠️ Implementation Plan¶

Week 1: - Float16 variants for GPU - Conversion helpers (float32 ↔ float16) - Benchmarking on mobile GPUs

Week 2: - Float64 variants for high precision - Fixed-point variants (Q15, Q31) - Validation and documentation

⚠️ Challenges¶

Precision Loss: float16 has limited range and precision
Saturation: Fixed-point requires careful overflow handling
Platform Support: Not all CPUs support fp16 efficiently
Mixed Precision: Converting between precisions has overhead

Status: ⏸️ Planned for future development Priority: MEDIUM - Useful for specialized use cases