5 citations · 7 across the 5 of their papers we have counts for
5 papers
Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference
Mengtian Yang, Zhekun Zhang, Mingheng Wu +3
Deploying large-scale LLM training and inference with optimal performance is exceptionally challenging due to a complex design space of parallelism strategies, system optimizations…
Vorion: A RISC-V GPU with Hardware-Accelerated 3D Gaussian Rendering and Training
Yipeng Wang, Mengtian Yang, Chieh-pu Lo +1
3D Gaussian Splatting (3DGS) has recently emerged as a foundational technique for real-time neural rendering, 3D scene generation, volumetric video (4D) capture. However, its rende…
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Zihui Xue, Yuedong Yang, Mengtian Yang +1
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Neve…
Characterizing and Demystifying the Implicit Convolution Algorithm on Commercial Matrix-Multiplication Accelerators
Yangjie Zhou, Mengtian Yang, Cong Guo +5
Many of today's deep neural network accelerators, e.g., Google's TPU and NVIDIA's tensor core, are built around accelerating the general matrix multiplication (i.e., GEMM). However…
NAAS: Neural Accelerator Architecture Search
Yujun Lin, Mengtian Yang, Song Han
Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus on sizing the numeric…