11 citations · 17 across the 5 of their papers we have counts for
5 papers
Proteus: Simulating the Performance of Distributed DNN Training
Jiangfei Duan, Xiuhong Li, Ping Xu +4
DNN models are becoming increasingly larger to achieve unprecedented accuracy, and the accompanying increased computation and memory requirements necessitate the employment of mass…
Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators
Xiaochen Hao, Zijian Ding, Jieming Yin +2
Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet…
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…
Self-paced Resistance Learning against Overfitting on Noisy Labels
Xiaoshuang Shi, Zhenhua Guo, Kang Li +2
Noisy labels composed of correct and corrupted ones are pervasive in practice. They might significantly deteriorate the performance of convolutional neural networks (CNNs), because…
CuMF_SGD: Fast and Scalable Matrix Factorization
Xiaolong Xie, Wei Tan, Liana L. Fong +1
Matrix factorization (MF) has been widely used in e.g., recommender systems, topic modeling and word embedding. Stochastic gradient descent (SGD) is popular in solving MF problems…