Siddharth Garg

research

∙ 07/14/2022

Characterizing and Optimizing End-to-End Systems for Private Inference

Increasing privacy concerns have given rise to Private Inference (PI). I...

Karthik Garimella, et al. ∙

research

∙ 06/29/2022

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale

The success of DNNs is driven by the counter-intuitive ability of over-p...

Akshaj Kumar Veldanda, et al. ∙

research

∙ 05/26/2022

Denial-of-Service Attacks on Learned Image Compression

Deep learning techniques have shown promising results in image compressi...

Kang Liu, et al. ∙

research

∙ 04/15/2022

Feature Compression for Rate Constrained Object Detection on the Edge

Recent advances in computer vision has led to a growth of interest in de...

Zhongzheng Yuan, et al. ∙

research

∙ 04/05/2022

Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis

Generating sub-optimal synthesis transformation sequences ("synthesis re...

Animesh Basak Chowdhury, et al. ∙

research

∙ 02/04/2022

Selective Network Linearization for Efficient Private Inference

Private inference (PI) enables inference directly on cryptographically s...

Minsu Cho, et al. ∙

research

∙ 11/04/2021

CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale

The privacy concerns of providing deep learning inference as a service h...

Karthik Garimella, et al. ∙

research

∙ 10/27/2021

Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification

The millimeter wave (mmWave) bands have attracted considerable attention...

Mingsheng Yin, et al. ∙

research

∙ 10/21/2021

OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis

Logic synthesis is a challenging and widely-researched combinatorial opt...

Animesh Basak Chowdhury, et al. ∙

research

∙ 07/20/2021

Single-Shot Compression for Hypothesis Testing

Enhanced processing power in the cloud allows constrained devices to off...

Fabrizio Carpi, et al. ∙

research

∙ 06/17/2021

Sphynx: ReLU-Efficient Network Design for Private Inference

The emergence of deep learning has been accompanied by privacy concerns ...

Minsu Cho, et al. ∙

research

∙ 06/15/2021

Circa: Stochastic ReLUs for Private Deep Learning

The simultaneous rise of machine learning as a service and concerns over...

Zahra Ghodsi, et al. ∙

research

∙ 05/20/2021

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

The globalization of the electronics supply chain is requiring effective...

Christian Pilato, et al. ∙

research

∙ 03/12/2021

Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles

Extracting interesting scenarios from real-world data as well as generat...

Zahra Ghodsi, et al. ∙

research

∙ 03/02/2021

DeepReDuce: ReLU Reduction for Fast Private Inference

The recent rise of privacy concerns has led researchers to devise method...

Nandan Kumar Jha, et al. ∙

research

∙ 02/11/2021

On Graph Matching Using Generalized Seed Side-Information

In this paper, matching pairs of stocahstically generated graphs in the ...

Mahshad Shariatnasab, et al. ∙

research

∙ 11/08/2020

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...

Naman Patel, et al. ∙

research

∙ 11/04/2020

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

This paper proposes a new defense against neural network backdooring att...

Hao Fu, et al. ∙

research

∙ 10/23/2020

On Evaluating Neural Network Backdoor Defenses

Deep neural networks (DNNs) demonstrate superior performance in various ...

Akshaj Veldanda, et al. ∙

research

∙ 10/11/2020

ASSURE: RTL Locking Against an Untrusted Foundry

Semiconductor design companies are integrating proprietary intellectual ...

Christian Pilato, et al. ∙

research

∙ 09/19/2020

Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Unprecedented data collection and sharing have exacerbated privacy conce...

Kang Liu, et al. ∙

research

∙ 08/30/2020

A Concentration of Measure Approach to Correlated Graph Matching

The graph matching problem emerges naturally in various applications suc...

Farhad Shirani, et al. ∙

research

∙ 08/27/2020

Adversarially Robust Learning via Entropic Regularization

In this paper we propose a new family of algorithms for training adversa...

Gauri Jagatap, et al. ∙

research

∙ 06/15/2020

CryptoNAS: Private Inference on a ReLU Budget

Machine learning as a service has given raise to privacy concerns surrou...

Zahra Ghodsi, et al. ∙

research

∙ 04/26/2020

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

Deep learning (DL) offers potential improvements throughout the CAD tool...

Kang Liu, et al. ∙

research

∙ 02/19/2020

NNoculation: Broad Spectrum and Targeted Treatment of Backdoored DNNs

This paper proposes a novel two-stage defense (NNoculation) against back...

Akshaj Kumar Veldanda, et al. ∙

research

∙ 01/20/2020

On the Joint Typicality of Permutations of Sequences of Random Variables

Permutations of correlated sequences of random variables appear naturall...

Farhad Shirani, et al. ∙

research

∙ 06/25/2019

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 ...

Kang Liu, et al. ∙

research

∙ 12/04/2018

Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation

In several settings of practical interest, two parties seek to collabora...

Siddharth Garg, et al. ∙

research

∙ 11/05/2018

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 ...

Imran Hafeez Abbassi, et al. ∙

research

∙ 07/02/2018

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

Deep neural networks (DNN) are increasingly being accelerated on applica...

Jeff Zhang, et al. ∙

research

∙ 05/30/2018

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

Deep neural networks (DNNs) provide excellent performance across a wide ...

Kang Liu, et al. ∙

research

∙ 02/11/2018

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...

Jeff Zhang, et al. ∙

research

∙ 02/11/2018

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...

Zhang, et al. ∙

research

∙ 11/04/2017

Optimal Checkpointing for Secure Intermittently-Powered IoT Devices

Energy harvesting is a promising solution to power Internet of Things (I...

Zahra Ghodsi, et al. ∙

research

∙ 10/28/2017

Reverse Engineering Camouflaged Sequential Integrated Circuits Without Scan Access

Integrated circuit (IC) camouflaging is a promising technique to protect...

Mohamed El Massad, et al. ∙

Siddharth Garg

Featured Co-authors