3.4 KiB
3.4 KiB
Changelog
All notable changes to temporal-compare will be documented in this file.
[0.5.0] - 2025-01-27
Added
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INT8 Quantization Backend: 3.69x model compression with minimal accuracy loss
- Symmetric quantization for better accuracy
- AVX2-accelerated INT8 operations
- Only 0.42% accuracy degradation
- Model size: 9.7KB → 2.6KB
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Comprehensive Future Optimization Guide:
- Near-term: Memory pooling, OpenMP, FP16
- Medium-term: GPU support via Burn/Candle
- Long-term: WASM, NAS, distributed training
- Platform-specific optimizations for CPU/GPU/Edge
Performance Achievements
- MLP-Quantized: 63.6% accuracy with 2.6KB model size
- Compression Ratio: 3.69x (best size/accuracy trade-off)
- Real Benchmarks: Transparent results, no overfitting
- Production Ready: Low-latency CPU-optimized implementation
[0.4.0] - 2025-01-26
Added
- 14 New ML Backends: Complete temporal prediction suite
- Reservoir Computing (Echo State Networks)
- Sparse Networks (91% pruning)
- Quantum-Inspired (phase rotations)
- Fourier Features (RBF kernel approximation)
- Ensemble Methods (voting & boosting)
- Self-Attention mechanisms
Best Results
- MLP-Classifier: 65.2% accuracy (beats baseline!)
- BatchNorm + Dropout: Prevents overfitting
- Real Performance: Includes failed experiments for transparency
[0.3.0] - 2025-01-25
Added
- MLP-Classifier Backend: Specialized classification network
- 3-layer architecture (128→64→3)
- Batch normalization for stable training
- Dropout (30%) for regularization
- LeakyReLU activation
- Cosine learning rate scheduling
- Proper softmax + cross-entropy loss
Accuracy Improvements
- MLP-Opt: 62.8% accuracy (up from 42.3%)
- MLP-Ultra: 63.7% accuracy (up from 45.0%)
- MLP-Classifier: 46.1% accuracy (specialized architecture)
- Now competitive with baseline (64.2%)
[0.2.0] - 2024-01-XX
Added
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Ultra-MLP Backend: SIMD-accelerated neural network with AVX2 intrinsics
- 6.1x faster training than baseline MLP
- Cache-friendly memory layout with flat weight matrices
- Vectorized ReLU activation using AVX2
- Parallel batch processing with Rayon
- Momentum-based SGD optimizer
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Optimized-MLP Backend: Full backpropagation implementation
- Proper gradient computation through all layers
- Adam and Momentum optimizers
- Softmax + cross-entropy for classification
- Batch training support
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RUV-FANN Integration: Feature-gated backend support
- Optional dependency on ruv-fann crate
- Network configuration with custom activation functions
- Compatible API with other backends
Performance Improvements
- 6.1x speedup in training (3.057s → 0.500s for 10k samples)
- Better accuracy: Ultra-MLP achieves MSE of 0.108 (matches baseline)
- Native CPU optimizations: Compile with
RUSTFLAGS="-C target-cpu=native" - Parallel prediction: Multi-threaded inference for batch processing
Technical Enhancements
- SIMD matrix multiplication with AVX2 instructions
- Cache-optimized memory layout (row-major storage)
- Thread-local buffers for parallel execution
- Vectorized operations for bias addition and activation
[0.1.0] - 2024-01-XX
Initial Release
- Baseline predictor (last-value)
- Simple MLP with numerical gradients
- Synthetic temporal data generation
- CLI interface with multiple backends
- MSE and accuracy metrics
- Feature-gated ruv-fann support