Abstract: Quantization techniques compress data by reducing large datasets into smaller code vector sets. This study introduces a vector quantization method within the Kernel Affine Projection ...

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 original version of this story appeared in Quanta Magazine. Imagine a town with two widget merchants. Customers prefer cheaper widgets, so the merchants must compete to set the lowest price.

Hi, here is the INC team from Intel. Thank you for developing this amazing project! Our team has developed AutoRound, a novel tuning-based quantization algorithm that delivers state-of-the-art ...

In trading, discussions often center on strategies, indicators, or market predictions. Yet behind the numbers lies a quieter factor that often determines whether a system can endure: position sizing.

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 ...

This is the official implementation of our paper GaussianImage, a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting. With compact 2D Gaussian representation and ...

A new technical paper titled “Cross-Layer Design of Vector-Symbolic Computing: Bridging Cognition and Brain-Inspired Hardware Acceleration” was published by researchers at Purdue University and ...

Ever wondered how social media platforms decide how to fill our feeds? They use algorithms, of course, but how do these algorithms work? A series of corporate leaks over the past few years provides a ...

New capabilities deliver up to 5X faster filtered vector search, improved ranking quality, and lower infrastructure costs to unlock scalable, cost-efficient AI applications SAN FRANCISCO--(BUSINESS ...