24 citations · 26 across the 6 of their papers we have counts for
6 papers
Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices
Dongho Yoon, Gungyu Lee, Jaewon Chang +3
Transformers have proven effective in language modeling but are limited by high computational and memory demands that grow quadratically with input sequence length. State space mod…
Debunking the CUDA Myth Towards GPU-based AI Systems
Yunjae Lee, Juntaek Lim, Jehyeon Bang +10
This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create…
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models
Yunjae Lee, Hyeseong Kim, Minsoo Rhu
Training recommendation systems (RecSys) faces several challenges as it requires the "data preprocessing" stage to preprocess an ample amount of raw data and feed them to the GPU f…
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures
Yunjae Lee, Jinha Chung, Minsoo Rhu
Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the e…
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performan…
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper ad…