In today’s fast-evolving landscape of artificial intelligence, the deployment of machine learning models on edge devices—smartphones, IoT sensors, embedded systems, and more—has become increasingly ...

Note: all results are measured with the torch JIT model on a single CPU core. Environment: Win10 22H2, Intel Core i5-10210U @ 1.60GHz 2.11GHz, 16GB RAM. Please refer to the provided script ...

Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green ...

self.register_buffer("weight", torch.zeros((out_features, in_features), dtype=torch.int8)) self.register_buffer("weight_scale", torch.zeros((out_features, 1), dtype ...

One-hot encoding is a prevalent method used to convert numeric variables into categorical variables. But one-hot encoding omits crucial quantitative data, which compromises the performance of ...

Abstract: TinyML enables the deployment of Machine Learning (ML) models on resource-constrained devices, addressing a growing need for efficient, low-power AI solutions. However, significant ...

Abstract: Generative diffusion models (GDMs) have emerged as potent tools for generating high-quality, creative content across various media, including audio, images, videos, and 3-D models. Their ...