# Service: Model Compression & Optimization

## metadata
- title: Model Compression & Optimization Service | Klyff
- route: /services/model-optimization
- specialization: TensorRT, ONNX, Quantization, Edge Optimization
- timeline: 4-6 weeks to production deployment

## Overview
Our edge AI engineering team profiles and optimizes customer neural networks to run at maximum FPS and minimal thermal dissipation on target silicon (NVIDIA Jetson Orin, Intel Xeon, Hailo, Google Coral, ARM Ethos). We balance feature extraction algorithms and model architectures for Bandwidth, Latency, Economics, Reliability, and Privacy (BLERP).

## Performance Benchmarks
- **Delivery Timeline:** Faster models on target edge device within 4-6 weeks.
- **Model Compression:** 2-8x smaller production-ready models.
- **Latency Acceleration:** 2-10x latency improvements on target hardware.
- **Thermal & Power Savings:** 30-70% power reduction compared to generic cloud deployments.

## What We Do

### 1. Quantization-Aware Training (QAT)
- Simulates low-precision arithmetic (INT8 or 4-bit) directly within the training cycle, ensuring models learn robustness against quantization noise.
- Prevents sharp accuracy drops common in post-training quantization (PTQ); crucial for memory-constrained platforms like Jetson Nano, Coral, and Cortex-M microcontrollers.
- Yields a production-ready model that is 2-8x smaller and 2-5x faster while maintaining safety-critical inspection standards.

### 2. Neural Architecture Search (NAS)
- Automates the discovery of optimal network topologies under strict physical device constraints (RAM, latency, compute, thermal headroom).
- Hardware-aware NAS enforces explicit hardware limits (e.g., <=256 KB RAM and <=5 ms latency) to uncover architectures that maximize accuracy without over-provisioning.
- Delivers compact, efficient architectures designed specifically for real-time factory floor conditions.

### 3. Hardware-Specific Optimization
- Leverages vendor-specific toolchains and micro-kernels: TensorRT, Coral Compiler, OpenVINO, CMSIS-NN, and Arm Ethos-U.
- Executes operator fusion, precision auto-calibration (FP16/INT8), and kernel auto-tuning for 2-10x latency improvements and 30-70% power reductions.
- Maintains identical model output behavior while completely accelerating execution paths.

### 4. Accuracy vs. Latency Tradeoff Profiling
- Characterizes how model precision shifts when pruning channels, lowering bit-widths, or modifying batch dimensions against real on-device throughput.
- Enables engineering teams to select evidence-based operating thresholds (e.g., 95% accuracy at 18 ms vs 97% at 32 ms) matching factory SLAs.
- Eliminates over-optimization waste while preventing defect detection escapes.

### 5. On-Device Performance Benchmarking
- Runs standardized test suites on physical production hardware to measure true end-to-end latency, throughput, memory pressure, and thermal dissipation.
- Pinpoints bottlenecks in camera I/O, image preprocessing, tensor inference, and postprocessing.
- Validates performance using real assembly-line image feeds.

## Engagement Model
1. **Initial Consultation:** Understand your network architecture, target hardware, and latency constraints.
2. **Optimization Exploration:** Evaluate pruning, QAT, and layer-fusion techniques across test batches.
3. **Hardware Benchmarking:** Profile latency, accuracy, and power on actual target edge devices.
4. **Final Tuning & Delivery:** Deliver compiled model artifacts, runtime wrappers, and verification test suites.