LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models

1 code implementation • 20 Jun 2022 • Gunho Park, Baeseong Park, Minsub Kim, Sungjae Lee, Jeonghoon Kim, Beomseok Kwon, Se Jung Kwon, Byeongwook Kim, Youngjoo Lee, Dongsoo Lee

Recent advances in self-supervised learning and the Transformer architecture have significantly improved natural language processing (NLP), achieving remarkably low perplexity.

Quantization Self-Supervised Learning

GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks

no code implementations • 1 Mar 2022 • Ranggi Hwang, Minhoo Kang, Jiwon Lee, Dongyun Kam, Youngjoo Lee, Minsoo Rhu

Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational.

Selective Deep Convolutional Neural Network for Low Cost Distorted Image Classification

no code implementations • 4 Jul 2018 • Minho Ha, Younghoon Byeon, Youngjoo Lee, Sunggu Lee

However, if there is distortion in the image, the classification accuracy can be significantly degraded, even with state-of-the-art neural networks.

Classification General Classification +1