Search Results for author: Makan Fardad

Found 11 papers, 5 papers with code

Loss Attitude Aware Energy Management for Signal Detection

no code implementations18 Jan 2023 Baocheng Geng, Chen Quan, Tianyun Zhang, Makan Fardad, Pramod K. Varshney

The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans.

energy management Management

Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting

no code implementations21 Dec 2021 Minghai Qin, Tianyun Zhang, Fei Sun, Yen-Kuang Chen, Makan Fardad, Yanzhi Wang, Yuan Xie

Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them from being deployed to many edge and internet-of-things (IoT) devices.

Graph Attention

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

no code implementations12 Apr 2020 Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shanglin Zhou, Minghai Qin, Fei Sun, Yen-Kuang Chen, Caiwen Ding, Makan Fardad, Yanzhi Wang

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i. e., static regularization-based pruning and dynamic regularization-based pruning.

Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness

no code implementations25 Sep 2019 Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li

The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.

Adversarial Attack Adversarial Robustness

Adversarial Attack Generation Empowered by Min-Max Optimization

1 code implementation NeurIPS 2021 Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li

In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.

Adversarial Attack Adversarial Robustness

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

2 code implementations23 Mar 2019 Shaokai Ye, Xiaoyu Feng, Tianyun Zhang, Xiaolong Ma, Sheng Lin, Zhengang Li, Kaidi Xu, Wujie Wen, Sijia Liu, Jian Tang, Makan Fardad, Xue Lin, Yongpan Liu, Yanzhi Wang

A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results.

Model Compression Quantization

StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs

1 code implementation29 Jul 2018 Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Xiaolong Ma, Ning Liu, Linfeng Zhang, Jian Tang, Kaisheng Ma, Xue Lin, Makan Fardad, Yanzhi Wang

Without loss of accuracy on the AlexNet model, we achieve 2. 58X and 3. 65X average measured speedup on two GPUs, clearly outperforming the prior work.

Model Compression

A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

3 code implementations ECCV 2018 Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, Yanzhi Wang

We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning.

Image Classification Network Pruning

Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers

1 code implementation15 Feb 2018 Tianyun Zhang, Shaokai Ye, Yi-Peng Zhang, Yanzhi Wang, Makan Fardad

We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM).

A Memristor-Based Optimization Framework for AI Applications

no code implementations18 Oct 2017 Sijia Liu, Yanzhi Wang, Makan Fardad, Pramod K. Varshney

In addition to ADMM, implementation of a customized power iteration (PI) method for eigenvalue/eigenvector computation using memristor crossbars is discussed.

BIG-bench Machine Learning

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