Abstract: Post-training quantization (PTQ) is an effective solution for deploying deep neural networks on edge devices with limited resources. PTQ is especially attractive because it does not require ...

Abstract: We investigate information-theoretic limits and design of communication under receiver quantization. Unlike most existing studies that focus on low-resolution quantization, this work is more ...

Hardware-accelerated YOLO11 object detection on Xilinx Zynq-7020 FPGA (PYNQ-Z2 board) using Keras 3, HGQ2, and HLS4ML. yolo11_zynq_deployment/ ├── config.yaml # Configuration file ├── requirements.txt ...

The 2025 Nobel Prize in Physics has been awarded to John Clarke, Michel H. Devoret, and John M. Martinis “for the discovery of macroscopic quantum tunneling and energy quantization in an electrical ...

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.

Soon to be the official tool for managing Python installations on Windows, the new Python Installation Manager picks up where the ‘py’ launcher left off. Python is a first-class citizen on Microsoft ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...

ABSTRACT: We establish quantum circuit complexity as a fundamental physical observable and prove that it satisfies an uncertainty relation with energy, analogous to Heisenberg’s canonical uncertainty ...

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 ...

Quantization is an essential technique in machine learning for compressing model data, which enables the efficient operation of large language models (LLMs). As the size and complexity of these models ...