It should probably come as no surprise to anyone that the images which we look at every day – whether printed or on a display – are simply illusions. That cat picture isn’t actually a cat, but rather ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

Quantization is a method of reducing the size of AI models so they can be run on more modest computers. The challenge is how to do this while still retaining as much of the model quality as possible, ...

Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality.

Struggling to understand energy quantization? In this MI Physics Lecture Chapter 8, you’ll learn the concept of energy quantization quickly and clearly with step-by-step explanations designed for ...

Experts At The Table: AI/ML is driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor ...