20 citations · 29 across the 5 of their papers we have counts for
5 papers
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
Seongmin Hong, Seungjae Moon, Junsoo Kim +4
Transformer is a deep learning language model widely used for natural language processing (NLP) services in datacenters. Among transformer models, Generative Pre-trained Transforme…
PROFET: Profiling-based CNN Training Latency Prophet for GPU Cloud Instances
Sungjae Lee, Yoonseo Hur, Subin Park +1
Training a Convolutional Neural Network (CNN) model typically requires significant computing power, and cloud computing resources are widely used as a training environment. However…
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
Gunho Park, Baeseong Park, Minsub Kim +7
Recent advances in self-supervised learning and the Transformer architecture have significantly improved natural language processing (NLP), achieving remarkably low perplexity. How…
SpotLake: Diverse Spot Instance Dataset Archive Service
Sungjae Lee, Jaeil Hwang, Kyungyong Lee
Public cloud service vendors provide a surplus of computing resources at a cheaper price as a spot instance. Despite the cheaper price, the spot instance can be forced to be shutdo…
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee +34
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less report…