1 code implementation • 23 Apr 2024 • Kaustubh Shivdikar, Nicolas Bohm Agostini, Malith Jayaweera, Gilbert Jonatan, Jose L. Abellan, Ajay Joshi, John Kim, David Kaeli
We introduce a rolling eviction strategy to mitigate data idling in on-chip memory as well as address the prevalent issue of memory bloat in sparse graph computations.
1 code implementation • 14 Dec 2023 • Hongwu Peng, Xi Xie, Kaustubh Shivdikar, MD Amit Hasan, Jiahui Zhao, Shaoyi Huang, Omer Khan, David Kaeli, Caiwen Ding
In this paper, we present MaxK-GNN, an advanced high-performance GPU training system integrating algorithm and system innovation.
1 code implementation • 1 Oct 2021 • Jude Haris, Perry Gibson, José Cano, Nicolas Bohm Agostini, David Kaeli
In this paper we propose SECDA, a new hardware/software co-design methodology to reduce design time of optimized DNN inference accelerators on edge devices with FPGAs.
no code implementations • ICCV 2021 • Zheng Zhan, Yifan Gong, Pu Zhao, Geng Yuan, Wei Niu, Yushu Wu, Tianyun Zhang, Malith Jayaweera, David Kaeli, Bin Ren, Xue Lin, Yanzhi Wang
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices.
no code implementations • 2 Jun 2020 • Maher Kachmar, David Kaeli
In today's enterprise storage systems, supported data services such as snapshot delete or drive rebuild can cause tremendous performance interference if executed inline along with heavy foreground IO, often leading to missing SLOs (Service Level Objectives).
no code implementations • 8 Sep 2019 • Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David Kaeli, Jennifer G. Dy
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition.
no code implementations • 13 Sep 2018 • Siyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David Kaeli, Peter Chin, Xue Lin
Based on the observations of the effect of test dropout rate on test accuracy and attack success rate, we propose a defensive dropout algorithm to determine an optimal test dropout rate given the neural network model and the attacker's strategy for generating adversarial examples. We also investigate the mechanism behind the outstanding defense effects achieved by the proposed defensive dropout.