Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing ...

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announces the release of a core technology for hybrid Quantum ...

MicroCloud Hologram Inc. has announced the creation of a noise-resistant Deep Quantum Neural Network (DQNN) architecture, which aims to advance quantum computing and enhance the efficiency of quantum ...

D-Wave Quantum Inc., a leader in quantum computing systems, software and services, today announced the release of a collection of open-source tools for developers that advance quantum computing ...

Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural ...