1 code implementation • 20 Dec 2023 • Anzhe Cheng, Zhenkun Wang, Chenzhong Yin, Mingxi Cheng, Heng Ping, Xiongye Xiao, Shahin Nazarian, Paul Bogdan
This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block.
no code implementations • 9 Dec 2023 • Shukai Duan, Nikos Kanakaris, Xiongye Xiao, Heng Ping, Chenyu Zhou, Nesreen K. Ahmed, Guixiang Ma, Mihai Capota, Theodore L. Willke, Shahin Nazarian, Paul Bogdan
We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.
no code implementations • 11 Oct 2023 • Chenzhong Yin, Mingxi Cheng, Xiongye Xiao, Xinghe Chen, Shahin Nazarian, Andrei Irimia, Paul Bogdan
Motivated by the intricacy of these collectives, we propose a neural network (NN) architecture inspired by the rules observed in nature's collective ensembles.
no code implementations • 25 Apr 2022 • Yao Xiao, Guixiang Ma, Nesreen K. Ahmed, Mihai Capota, Theodore Willke, Shahin Nazarian, Paul Bogdan
To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the complexity of high-level programs down to the universal intermediate representation, extracting the specific computational patterns and predicting which code segments would run best on a specific core in heterogeneous hardware platforms.
no code implementations • 8 Nov 2021 • Mingxi Cheng, Junyao Zhang, Shahin Nazarian, Jyotirmoy Deshmukh, Paul Bogdan
Many intelligent transportation systems are multi-agent systems, i. e., both the traffic participants and the subsystems within the transportation infrastructure can be modeled as interacting agents.
1 code implementation • 28 Jan 2021 • Mingxi Cheng, Shahin Nazarian, Paul Bogdan
VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components.
no code implementations • 9 Oct 2020 • Guixiang Ma, Yao Xiao, Theodore L. Willke, Nesreen K. Ahmed, Shahin Nazarian, Paul Bogdan
High-level applications, such as machine learning, are evolving from simple models based on multilayer perceptrons for simple image recognition to much deeper and more complex neural networks for self-driving vehicle control systems. The rapid increase in the consumption of memory and computational resources by these models demands the use of multi-core parallel systems to scale the execution of the complex emerging applications that depend on them.
1 code implementation • 3 Jul 2020 • Ghasem Pasandi, Mackenzie Peterson, Moises Herrera, Shahin Nazarian, Massoud Pedram
This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level.
no code implementations • 13 Feb 2020 • Mohammad Saeed Abrishami, Massoud Pedram, Shahin Nazarian
The miniaturization of transistors down to 5nm and beyond, plus the increasing complexity of integrated circuits, significantly aggravate short channel effects, and demand analysis and optimization of more design corners and modes.
no code implementations • 13 Feb 2020 • Mohammad Saeed Abrishami, Hao Ge, Justin F. Calderon, Massoud Pedram, Shahin Nazarian
The shrinking of transistor geometries as well as the increasing complexity of integrated circuits, significantly aggravate nonlinear design behavior.
no code implementations • 12 Feb 2020 • Mohammad Saeed Abrishami, Amir Erfan Eshratifar, David Eigen, Yanzhi Wang, Shahin Nazarian, Massoud Pedram
However, fine-tuning a transfer model with data augmentation in the raw input space has a high computational cost to run the full network for every augmented input.
no code implementations • 25 Sep 2019 • Mingxi Cheng, Yizhi Li, Shahin Nazarian, Paul Bogdan
However, the vigorous growth of social media contributes to the fast-spreading and far-reaching rumors.
no code implementations • 1 Feb 2019 • Ghasem Pasandi, Shahin Nazarian, Massoud Pedram
Approximate Logic Synthesis (ALS) is the process of synthesizing and mapping a given Boolean network to a library of logic cells so that the magnitude/rate of error between outputs of the approximate and initial (exact) Boolean netlists is bounded from above by a predetermined total error threshold.
Hardware Architecture
no code implementations • 6 Jul 2017 • Mahdi Nazemi, Shahin Nazarian, Massoud Pedram
Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e. g. Bayesian neural networks.