Memory swizzling is the quiet tax that every hierarchical-memory accelerator pays. It is fundamental to how GPUs, TPUs, NPUs, ...

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Abstract: Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This ...

Abstract: Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the ...

This is currently very much WIP. These custom nodes provide support for model files stored in the GGUF format popularized by llama.cpp. While quantization wasn't feasible for regular UNET models ...

This repository contains the official PyTorch implementation for the CVPR 2025 paper "APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision ...