no code implementations • 2 May 2024 • Xun Jiao, Fred Lin, Harish D. Dixit, Joel Coburn, Abhinav Pandey, Han Wang, Venkat Ramesh, Jianyu Huang, Wang Xu, Daniel Moore, Sriram Sankar
PVF can provide pivotal insights to AI hardware designers in balancing the tradeoff between fault protection and performance/efficiency such as mapping vulnerable AI parameter components to well-protected hardware modules.
no code implementations • 20 Jul 2023 • Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao, Muhammad Shafique, Kiamal Pekmestzi, Dimitrios Soudris
The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches.
no code implementations • 17 Jul 2023 • Dongning Ma, Xun Jiao, Fred Lin, Mengshi Zhang, Alban Desmaison, Thomas Sellinger, Daniel Moore, Sriram Sankar
Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality.
no code implementations • 7 Dec 2022 • Ruixuan Wang, Fred Lin, Daniel Moore, Sriram Sankar, Xun Jiao
Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the first time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy.
no code implementations • 23 Sep 2022 • Dongning Ma, Pengfei Zhao, Xun Jiao
Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation.
no code implementations • 24 Jul 2022 • Dongning Ma, Xun Jiao
Hyperdimensional Computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm.
no code implementations • 25 Mar 2022 • Ruixuan Wang, Dongning Ma, Xun Jiao
Ensemble learning is a classical learning method utilizing a group of weak learners to form a strong learner, which aims to increase the accuracy of the model.
no code implementations • 11 Feb 2022 • Junhuan Yang, Yi Sheng, Sizhe Zhang, Ruixuan Wang, Kenneth Foreman, Mikell Paige, Xun Jiao, Weiwen Jiang, Lei Yang
On the Clintox dataset, which tries to learn features from developed drugs that passed/failed clinical trials for toxicity reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC scores, which are 0. 80% higher than the manually designed HDC and 9. 75% higher than conventional neural networks.
no code implementations • 7 Feb 2022 • Dongning Ma, Sizhe Zhang, Xun Jiao
We formulate the model development of HDC as a problem that can be used in blockchain mining.
no code implementations • 5 Jun 2021 • Dongning Ma, Rahul Thapa, Xun Jiao
In this paper, we propose a viable alternative to existing learning methods by presenting MoleHD, a method based on brain-inspired hyperdimensional computing (HDC) for molecular property prediction.
no code implementations • 26 May 2021 • Rahul Thapa, Dongning Ma, Xun Jiao
In this paper, we systematically expose the unexpected or incorrect behaviors of HDC models by developing HDXplore, a blackbox differential testing-based framework.
no code implementations • 15 Mar 2021 • Dongning Ma, Jianmin Guo, Yu Jiang, Xun Jiao
Using handwritten digit classification as an example, we show that HDTest can generate thousands of adversarial inputs with negligible perturbations that can successfully fool HDC models.
no code implementations • 8 Feb 2021 • Dongning Ma, Meltem Izzetoglu, Roee Holtzer, Xun Jiao
However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet.
no code implementations • 13 Jul 2020 • Mauro J. Sanchirico III, Xun Jiao, C. Nataraj
Polynomial expansions are important in the analysis of neural network nonlinearities.
no code implementations • 30 Jun 2018 • Yuanliang Chen, Yu Jiang, Jie Liang, Mingzhe Wang, Xun Jiao
For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications.
Software Engineering