Model Compression

Published:

Model compression is the process of making AI models smaller and faster so they’re easier to run, especially on devices with limited power or memory. The idea is to reduce the size of the model while keeping its accuracy as close as possible to the original. This usually involves shrinking the number of parameters, speeding up the model’s calculations, and lowering memory or energy use so it can run smoothly on phones, embedded hardware, or edge devices.

There are several common ways to compress a model. Pruning removes weights or neurons that don’t contribute much. Quantization reduces the precision of numbers inside the model to make computations lighter. Knowledge distillation trains a smaller “student” model to imitate a large, high-quality “teacher” model. A good compression setup keeps predictions nearly the same while improving speed and reducing cost.

Follow us on Facebook and LinkedIn to keep abreast of our latest news and articles