slimnet 0.2.0

Hardware-Aware Neural Architecture Search with compression and compiler tuning

slimnet

Hardware-Aware Neural Architecture Search & Edge AI Deployment Toolkit

End-to-end pipeline to optimize deep learning models for embedded and edge hardware — from differentiable NAS to ONNX deployment.

Installation

pip install slimnet

Commands

# Full optimization pipeline
slimnet --model model.pth --platform local_pc --dataset cifar10

# Latency prediction
slimnet-predict --model model.onnx --platform local_pc

Stages

• Stage 1 — Differentiable NAS (MixedOp + Gumbel-Softmax)
• Stage 2 — Structured channel pruning
• Stage 3 — Post-Training Quantization (FP16 / INT8) [opt-in]
• Stage 4 — ORT compiler tuning [opt-in]

Supported platforms

• local_pc — Intel Core Ultra 7 165U
• raspberry_pi — ARM Cortex-A72
• jetson_nano — NVIDIA Maxwell GPU
• nxp_imx8 — NXP i.MX8M Plus

License

MIT