Siddharth Garg

Single-Shot Compression for Hypothesis Testing

Enhanced processing power in the cloud allows constrained devices to off...
07/20/2021 ∙ by Fabrizio Carpi, et al. ∙ 0 ∙ share

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Sphynx: ReLU-Efficient Network Design for Private Inference

The emergence of deep learning has been accompanied by privacy concerns ...
06/17/2021 ∙ by Minsu Cho, et al. ∙ 0 ∙ share

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Circa: Stochastic ReLUs for Private Deep Learning

The simultaneous rise of machine learning as a service and concerns over...
06/15/2021 ∙ by Zahra Ghodsi, et al. ∙ 0 ∙ share

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On the Optimization of Behavioral Logic Locking for High-Level Synthesis

The globalization of the electronics supply chain is requiring effective...
05/20/2021 ∙ by Christian Pilato, et al. ∙ 0 ∙ share

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Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles

Extracting interesting scenarios from real-world data as well as generat...
03/12/2021 ∙ by Zahra Ghodsi, et al. ∙ 9 ∙ share

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DeepReDuce: ReLU Reduction for Fast Private Inference

The recent rise of privacy concerns has led researchers to devise method...
03/02/2021 ∙ by Nandan Kumar Jha, et al. ∙ 0 ∙ share

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On Graph Matching Using Generalized Seed Side-Information

In this paper, matching pairs of stocahstically generated graphs in the ...
02/11/2021 ∙ by Mahshad Shariatnasab, et al. ∙ 0 ∙ share

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Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems

We show that by controlling parts of a physical environment in which a p...
11/08/2020 ∙ by Naman Patel, et al. ∙ 10 ∙ share

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Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection

This paper proposes a new defense against neural network backdooring att...
11/04/2020 ∙ by Hao Fu, et al. ∙ 0 ∙ share

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On Evaluating Neural Network Backdoor Defenses

Deep neural networks (DNNs) demonstrate superior performance in various ...
10/23/2020 ∙ by Akshaj Veldanda, et al. ∙ 0 ∙ share

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ASSURE: RTL Locking Against an Untrusted Foundry

Semiconductor design companies are integrating proprietary intellectual ...
10/11/2020 ∙ by Christian Pilato, et al. ∙ 0 ∙ share

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Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Unprecedented data collection and sharing have exacerbated privacy conce...
09/19/2020 ∙ by Kang Liu, et al. ∙ 6 ∙ share

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A Concentration of Measure Approach to Correlated Graph Matching

The graph matching problem emerges naturally in various applications suc...
08/30/2020 ∙ by Farhad Shirani, et al. ∙ 0 ∙ share

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Adversarially Robust Learning via Entropic Regularization

In this paper we propose a new family of algorithms for training adversa...
08/27/2020 ∙ by Gauri Jagatap, et al. ∙ 6 ∙ share

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CryptoNAS: Private Inference on a ReLU Budget

Machine learning as a service has given raise to privacy concerns surrou...
06/15/2020 ∙ by Zahra Ghodsi, et al. ∙ 0 ∙ share

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Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks

Deep learning (DL) offers potential improvements throughout the CAD tool...
04/26/2020 ∙ by Kang Liu, et al. ∙ 1 ∙ share

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NNoculation: Broad Spectrum and Targeted Treatment of Backdoored DNNs

This paper proposes a novel two-stage defense (NNoculation) against back...
02/19/2020 ∙ by Akshaj Kumar Veldanda, et al. ∙ 0 ∙ share

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On the Joint Typicality of Permutations of Sequences of Random Variables

Permutations of correlated sequences of random variables appear naturall...
01/20/2020 ∙ by Farhad Shirani, et al. ∙ 0 ∙ share

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Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

There is substantial interest in the use of machine learning (ML) based ...
06/25/2019 ∙ by Kang Liu, et al. ∙ 1 ∙ share

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Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation

In several settings of practical interest, two parties seek to collabora...
12/04/2018 ∙ by Siddharth Garg, et al. ∙ 0 ∙ share

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TrojanZero: Switching Activity-Aware Design of Undetectable Hardware Trojans with Zero Power and Area Footprint

Conventional Hardware Trojan (HT) detection techniques are based on the ...
11/05/2018 ∙ by Imran Hafeez Abbassi, et al. ∙ 0 ∙ share

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FATE: Fast and Accurate Timing Error Prediction Framework for Low Power DNN Accelerator Design

Deep neural networks (DNN) are increasingly being accelerated on applica...
07/02/2018 ∙ by Jeff Zhang, et al. ∙ 0 ∙ share

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Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Deep neural networks (DNNs) provide excellent performance across a wide ...
05/30/2018 ∙ by Kang Liu, et al. ∙ 0 ∙ share

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ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators

Hardware accelerators are being increasingly deployed to boost the perfo...
02/11/2018 ∙ by Jeff Zhang, et al. ∙ 0 ∙ share

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Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator

Due to their growing popularity and computational cost, deep neural netw...
02/11/2018 ∙ by Zhang, et al. ∙ 0 ∙ share

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Optimal Checkpointing for Secure Intermittently-Powered IoT Devices

Energy harvesting is a promising solution to power Internet of Things (I...
11/04/2017 ∙ by Zahra Ghodsi, et al. ∙ 0 ∙ share

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Reverse Engineering Camouflaged Sequential Integrated Circuits Without Scan Access

Integrated circuit (IC) camouflaging is a promising technique to protect...
10/28/2017 ∙ by Mohamed El Massad, et al. ∙ 0 ∙ share

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Siddharth Garg

Featured Co-authors

Anima Anandkumar

Muhammad Shafique

Chinmay Hegde

Minsu Cho

Zhang

Elza Erkip

Kang Liu

Yuzhe Ma

Bei Yu

Faiq Khalid

Ramesh Karri

Single-Shot Compression for Hypothesis Testing

Sphynx: ReLU-Efficient Network Design for Private Inference

Circa: Stochastic ReLUs for Private Deep Learning

On the Optimization of Behavioral Logic Locking for High-Level Synthesis

Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles

DeepReDuce: ReLU Reduction for Fast Private Inference

On Graph Matching Using Generalized Seed Side-Information

Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems

Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection

On Evaluating Neural Network Backdoor Defenses

ASSURE: RTL Locking Against an Untrusted Foundry

Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

A Concentration of Measure Approach to Correlated Graph Matching

Adversarially Robust Learning via Entropic Regularization

CryptoNAS: Private Inference on a ReLU Budget

Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks

NNoculation: Broad Spectrum and Targeted Treatment of Backdoored DNNs

On the Joint Typicality of Permutations of Sequences of Random Variables

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation

TrojanZero: Switching Activity-Aware Design of Undetectable Hardware Trojans with Zero Power and Area Footprint

FATE: Fast and Accurate Timing Error Prediction Framework for Low Power DNN Accelerator Design

Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators

Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator

Optimal Checkpointing for Secure Intermittently-Powered IoT Devices

Reverse Engineering Camouflaged Sequential Integrated Circuits Without Scan Access