wifi-densepose/vendor/sublinear-time-solver/crates/neural-network-implementation/plan/IMPLEMENTATION_PLAN.md

Neural Network Implementation Plan

Temporal Micro-Net with Sublinear Solver Integration

Executive Summary

Implementation of a temporal prediction neural network system that combines traditional micro-nets with sublinear solver gating for improved latency and stability in short-horizon predictions. The system will be deployed to HuggingFace with comprehensive benchmarking.

Project Structure

neural-network-implementation/
├── plan/                    # Project planning documents
│   ├── IMPLEMENTATION_PLAN.md
│   ├── architecture.md
│   └── milestones.md
├── src/                     # Source code
│   ├── models/             # Neural network models
│   │   ├── traditional_micronet.py
│   │   ├── temporal_solver_net.py
│   │   └── base_model.py
│   ├── solvers/            # Sublinear solver integration
│   │   ├── solver_gate.py
│   │   ├── projection.py
│   │   └── pagerank_selector.py
│   ├── data/               # Data processing
│   │   ├── preprocessing.py
│   │   ├── loaders.py
│   │   └── augmentation.py
│   ├── training/           # Training pipelines
│   │   ├── trainer.py
│   │   ├── active_selection.py
│   │   └── callbacks.py
│   └── inference/          # Inference engine
│       ├── predictor.py
│       ├── kalman_filter.py
│       └── quantization.py
├── tests/                   # Test suite
│   ├── unit/
│   ├── integration/
│   └── performance/
├── models/                  # Saved model checkpoints
├── data/                    # Dataset storage
├── benchmarks/              # Benchmark results
├── configs/                 # Configuration files
│   ├── A_traditional.yaml
│   ├── B_temporal_solver.yaml
│   └── common.yaml
└── docs/                    # Documentation

Implementation Phases

Phase 1: Core Infrastructure (Day 1-2)

1. Base Model Architecture

   • Abstract base class for micro-nets
   • Common interfaces for training/inference
   • Configuration management system

2. Data Pipeline

   • Preprocessing for time series data
   • Sliding window generation
   • Z-score normalization
   • Train/val/test temporal splits

3. Sublinear Solver Integration

   • Wrapper for solve_projection API
   • Certificate error handling
   • Budget management

Phase 2: Model Implementation (Day 2-3)

1. System A - Traditional Micro-Net

   • Residual GRU implementation
   • TCN alternative
   • FP32 training, INT8 inference
   • 128ms window, 500ms horizon prediction

2. System B - Temporal Solver Net

   • Same architecture as System A
   • Kalman filter prior integration
   • Residual learning approach
   • Solver gate implementation
   • Active selection with PageRank

Phase 3: Training Pipeline (Day 3-4)

1. Standard Training

   • Adam optimizer setup
   • MSE loss with smoothness penalty
   • Early stopping on validation
   • Batch size 256, 15 epochs

2. Active Selection Training

   • kNN graph construction
   • PageRank scoring
   • Sample selection strategy
   • Error-guided sampling

Phase 4: Inference Optimization (Day 4-5)

1. Latency Optimization

   • INT8 quantization
   • Single-core CPU optimization
   • Memory pinning
   • Thread locking

2. Real-time Processing

   • Sub-millisecond inference
   • Certificate validation
   • Safe fallback mechanisms

Phase 5: Benchmarking & Evaluation (Day 5-6)

1. Performance Metrics

   • MSE at 500ms horizon
   • P90/P99 absolute error
   • P50/P99.9 latency
   • Gate pass rate
   • Certificate error tracking

2. A/B Testing Framework

   • Paired t-tests
   • Mann-Whitney U tests
   • Effect size calculation
   • Statistical significance

Phase 6: HuggingFace Deployment (Day 6-7)

1. Model Packaging

   • Model card creation
   • Dataset documentation
   • Training scripts
   • Inference examples

2. Repository Setup

   • Model weights upload
   • Configuration files
   • README and documentation
   • Demo application

Technical Specifications

Model Architecture

common:
  horizon_ms: 500
  window_ms: 128
  sample_rate_hz: 2000
  features: [x, y, vx, vy]
  quantize: int8
  optimizer: adam
  lr: 1e-3
  batch: 256
  epochs: 15

A_traditional:
  model: micro_gru
  hidden: 32

B_temporal_solver:
  model: micro_gru
  hidden: 32
  prior: kalman
  solver_gate:
    eps: 0.02
    budget: 200000
  active_selection:
    k: 15
    eps: 0.03

Performance Targets

• Latency Budget (per tick):
  • Ingest: 0.10ms
  • Prior: 0.10ms
  • Network: 0.30ms
  • Gate: 0.20ms
  • Actuation: 0.10ms
  • Total P99.9 ≤ 0.90ms

Success Criteria

1. System B reduces P99.9 latency by ≥20% OR
2. System B reduces P99 error by ≥15% with equal latency
3. Gate pass rate ≥90% with avg cert.error ≤0.02

Dependencies

# Core
pytorch >= 2.0
numpy >= 1.24
scipy >= 1.10
scikit-learn >= 1.3

# Optimization
onnx >= 1.14
onnxruntime >= 1.16
torch-quantization >= 2.1

# Sublinear Solver
sublinear-time-solver >= 0.1.0

# Deployment
huggingface-hub >= 0.19
transformers >= 4.35
accelerate >= 0.24

# Monitoring
tensorboard >= 2.14
wandb >= 0.16

Risk Mitigation

1. Performance Risks

   • Fallback to traditional method if solver fails
   • Adjustable epsilon parameters
   • Multiple budget configurations

2. Training Risks

   • Checkpoint saving every epoch
   • Multiple seed runs
   • Gradient clipping

3. Deployment Risks

   • Thorough testing on diverse data
   • Graceful degradation
   • Version control for models

Testing Strategy

1. Unit Tests

   • Model components
   • Solver integration
   • Data processing

2. Integration Tests

   • End-to-end training
   • Inference pipeline
   • A/B comparison

3. Performance Tests

   • Latency benchmarks
   • Memory usage
   • Throughput testing

Documentation Requirements

1. Code Documentation

   • Docstrings for all functions
   • Type hints
   • Inline comments for complex logic

2. User Documentation

   • Installation guide
   • Training tutorial
   • Inference examples
   • API reference

3. HuggingFace Model Card

   • Model description
   • Training procedure
   • Evaluation results
   • Limitations and biases
   • Citation information

Deliverables

1. Week 1

   • Complete implementation of Systems A & B
   • Training pipelines
   • Basic evaluation

2. Week 2

   • Full benchmarking suite
   • Statistical analysis
   • HuggingFace deployment
   • Final documentation

Success Metrics

• ✅ Both systems fully implemented
• ✅ All tests passing
• ✅ Performance targets met
• ✅ HuggingFace model published
• ✅ Documentation complete
• ✅ Reproducible results