Low Precision Representation

Low-precision representation focuses on reducing the computational cost and memory footprint of machine learning models by using fewer bits to represent weights and activations, thereby enabling deployment on resource-constrained devices. Current research emphasizes developing quantization techniques that minimize accuracy loss, including methods that strategically employ mixed-precision representations and adapt to outliers or model sensitivities. This area is crucial for advancing large language models, robotics control systems, and other applications where high-performance is needed despite limited hardware resources, improving efficiency and scalability.