Performance optimization and modern ML backends

## Summary
Optimize HTFA implementation for maximum performance using modern ML frameworks and hardware acceleration.

## Tasks
### Modern Framework Integration
- [ ] Investigate JAX implementation for automatic differentiation
- [ ] Explore NumPy array API standard compliance
- [ ] Consider PyTorch backend for GPU acceleration
- [ ] Evaluate Numba JIT compilation for critical loops
- [ ] Research CuPy for direct CUDA implementations

### Algorithm Optimizations
- [ ] Profile existing implementation to identify bottlenecks
- [ ] Optimize matrix operations and memory usage
- [ ] Implement efficient sparse matrix support
- [ ] Add support for mini-batch processing
- [ ] Investigate alternative optimization algorithms (ADAM, etc.)

### Hardware Acceleration
- [ ] Add GPU support via CuPy or PyTorch
- [ ] Implement Apple Metal Performance Shaders support
- [ ] Add multi-threading support for CPU-bound operations
- [ ] Optimize for modern CPU architectures (AVX, etc.)

### Scalability Improvements
- [ ] Add distributed computing support (Dask/Ray)
- [ ] Implement online/streaming algorithms for large datasets
- [ ] Add checkpointing for long-running optimizations
- [ ] Implement progressive refinement strategies

## Performance Targets
- 10x speedup over current implementation
- Support for datasets with >100k voxels
- GPU acceleration for compatible operations
- Memory usage scaling improvements

## Acceptance Criteria
- Comprehensive performance benchmarks
- Backward compatibility maintained
- Optional dependencies for acceleration frameworks
- Performance improvements validated against BrainIAK