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 ...

Abstract: Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This ...

Abstract: The huge memory and computing costs of deep neural networks (DNNs) greatly hinder their deployment on resource-constrained devices with high efficiency. Quantization has emerged as an ...

Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...

The shaping of light using spatial light modulators (SLMs) is an established technology for advanced three-dimensional (3D) displays 1 and micro-manipulation 2. In the SLM an incident beam of coherent ...

This example shows how to perform quantization aware training as a way to prepare a network for quantization. Quantization aware training is a method that can help recover accuracy lost due to ...

1 Department of Physics, Hanyang University, Seoul, South Korea 2 Department of Applied Mathematics, Philander Smith College, Little Rock, AR, USA The method of Park [1] has an undesirable feature: it ...