Explore the significance of model quantization in AI, its methods, and impact on computational efficiency, as detailed by NVIDIA's expert insights. As artificial intelligence (AI) models grow in ...

The deployment of Spiking Neural Networks (SNNs) on resource-constrained edge devices is hindered by a critical algorithm-hardware mismatch: a fundamental trade-off between the accuracy degradation ...

Huawei’s Zurich Computing Systems Laboratory has released SINQ (Sinkhorn Normalization Quantization), an open-source quantization method that reduces the memory requirements of large language models ...

This project aims to integrate BBQ into the OpenSearch k-NN plugin to offer users a memory-efficient alternative, ideal for large-scale vector workloads in constrained compute environments. The ...

ABSTRACT: Breast cancer remains one of the most prevalent diseases that affect women worldwide. Making an early and accurate diagnosis is essential for effective treatment. Machine learning (ML) ...

ABSTRACT: Breast cancer remains one of the most prevalent diseases that affect women worldwide. Making an early and accurate diagnosis is essential for effective treatment. Machine learning (ML) ...

Post-training quantization (PTQ) focuses on reducing the size and improving the speed of large language models (LLMs) to make them more practical for real-world use. Such models require large data ...

Using the fundamental framework of the thin-layer quantization method, we discuss the non-relativistic limit of the Schrödinger-Dirac equation for a particle constrained to move on a curved surface.