no code implementations • 1 Jul 2022 • Zhongnan Qu, Syed Shakib Sarwar, Xin Dong, Yuecheng Li, Ekin Sumbul, Barbara De Salvo
The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource constraints.
no code implementations • 8 Jun 2022 • Vivek Parmar, Syed Shakib Sarwar, Ziyun Li, Hsien-Hsin S. Lee, Barbara De Salvo, Manan Suri
Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse.
no code implementations • 14 Mar 2022 • Jorge Gomez, Saavan Patel, Syed Shakib Sarwar, Ziyun Li, Raffaele Capoccia, Zhao Wang, Reid Pinkham, Andrew Berkovich, Tsung-Hsun Tsai, Barbara De Salvo, Chiao Liu
Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the next generation computing platform.
no code implementations • 9 Mar 2022 • Dominika Przewlocka-Rus, Syed Shakib Sarwar, H. Ekin Sumbul, Yuecheng Li, Barbara De Salvo
Eventually, the experiments showed that for low bit width precision, non-uniform quantization performs better than uniform, and at the same time, PoT quantization vastly reduces the computational complexity of the neural network.
no code implementations • 7 May 2019 • Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar, Chankyu Lee, Wachirawit Ponghiran, Kaushik Roy
In this work, we present, for the first time, a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking Neural Network (SNN) under state-of-the-art adversarial tests.
no code implementations • 15 Mar 2019 • Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm.
no code implementations • 7 Dec 2017 • Syed Shakib Sarwar, Aayush Ankit, Kaushik Roy
We propose an efficient training methodology and incrementally growing DCNN to learn new tasks while sharing part of the base network.
no code implementations • 12 May 2017 • Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy
This combination creates a balanced system that gives better training performance in terms of energy and time, compared to the standalone CNN (without any Gabor kernels), in exchange for tolerable accuracy degradation.
no code implementations • 27 Feb 2016 • Gopalakrishnan Srinivasan, Parami Wijesinghe, Syed Shakib Sarwar, Akhilesh Jaiswal, Kaushik Roy
Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0. 5%) in accuracy (22nm predictive technology).
no code implementations • 27 Feb 2016 • Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy
Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks.