This directory provides examples of how to deploy Deep CNNs on FPGAs using Xilinx Python APIs. All examples provided within this directory exercise precompiled versions of GoogLeNet or ResNet50 whose ...

The Model Optimizer - Windows (ModelOpt-Windows) is engineered to deliver advanced model compression techniques, including quantization, to Windows RTX PC systems. Specifically tailored to meet the ...

Abstract: Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining suboptimal performance ...

A quantization scheme based on the extension of phase space with application of constrained quantization technic is considered. The obtained method is similar to the geometric quantization. For ...

Abstract: This paper studies distributed algorithms for (strongly convex) composite optimization problems over mesh networks, subject to quantized communications. Instead of focusing on a specific ...

Canonical quantization of gravitational systems is obstructed by the problem of time. Due to diffeomorphism symmetry the Hamiltonian vanishes: dynamics with respect to a background time parameter ...

This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers.

Deep product quantization networks (DPQNs) have been successfully used in image retrieval tasks, due to their powerful feature extraction ability and high efficiency of encoding high-dimensional ...