𝗔𝗡 𝗢𝗩𝗘𝗥𝗩𝗜𝗘𝗪 𝗢𝗙 𝗡𝗘𝗨𝗥𝗔𝗟 𝗡𝗘𝗧𝗪𝗢𝗥𝗞 𝗖𝗢𝗠𝗣𝗥𝗘𝗦𝗦𝗜𝗢𝗡
Large AI models take too much memory. They run slowly on mobile devices. They cost too much to host in the cloud.
Neural network compression solves these problems. It makes models smaller and faster without losing much accuracy.
You should know these three main methods:
- Pruning: This removes unnecessary connections or neurons. It cuts out the parts of the model that do not help much.
- Quantization: This reduces the precision of the numbers used in the model. Instead of using complex decimals, it uses simpler numbers. This saves huge amounts of space.
- Knowledge Distillation: This trains a small model to mimic a large model. The small model learns from the big one. It gets similar results with fewer resources.
Using these methods helps you deploy AI on edge devices. You get better speed and lower costs.
Source: https://dev.to/paperium/an-overview-of-neural-network-compression-1hp0
Optional learning community: https://t.me/GyaanSetuAi