FrostNet: Towards Quantization-Aware Network Architecture Search

A resource-conscious neural network implementation for MCUs

🚀 Leveraging advanced RNN with LSTM for efficient, real-time anomaly detection in IoT networks, optimized for performance in resource-constrained environments.

eve-mli: making learning interesting

Learning Path: RISC-V & Advanced Edge AI on SiFive FE310-G002 SoC | 32-bit RISC-V | 320 MHz | 16KB L1 Instruction Cache | 128Mbit (16MB) QSPI Flash | 4-stage pipeline

Clean C language version of quantizing llama2 model and running quantized llama2 model

Code for ICCV2025 paper 'Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers'

This project demonstrates the impact of model design choices on both energy consumption and economic cost. It analyzes the weight importance within a neural network, estimates the total FLOPs required for inference, and explores how quantization and pruning affect resource efficiency.

Learning Path: RISC-V & Advanced Edge AI on SiFive FE310-G002 SoC | 32-bit RISC-V | 320 MHz | 16KB L1 Instruction Cache | 128Mbit (16MB) QSPI Flash | 4-stage pipeline

A Tutorial Notebook to Quantization in Machine Learning