b'Niraj K. Jha'

research

∙ 08/16/2023

BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration

Researchers constantly strive to explore larger and more complex search ...

0 Shikhar Tuli, et al. ∙

research

∙ 05/27/2023

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

Deployment of Transformer models on the edge is increasingly challenging...

0 Hongjie Wang, et al. ∙

research

∙ 05/26/2023

Im-Promptu: In-Context Composition from Image Prompts

Large language models are few-shot learners that can solve diverse tasks...

0 Bhishma Dedhia, et al. ∙

research

∙ 05/09/2023

DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors

Modern advances in machine learning (ML) and wearable medical sensors (W...

0 Chia-Hao Li, et al. ∙

research

∙ 05/08/2023

SECRETS: Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention

The randomized controlled trial (RCT) is the gold standard for estimatin...

0 Sayeri Lala, et al. ∙

research

∙ 03/27/2023

TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference

Automated co-design of machine learning models and evaluation hardware i...

0 Shikhar Tuli, et al. ∙

research

∙ 03/24/2023

EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms

Automated design of efficient transformer models has recently attracted ...

0 Shikhar Tuli, et al. ∙

research

∙ 02/28/2023

AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers

Self-attention-based transformer models have achieved tremendous success...

0 Shikhar Tuli, et al. ∙

research

∙ 01/11/2023

ML-FEED: Machine Learning Framework for Efficient Exploit Detection (Extended version)

Machine learning (ML)-based methods have recently become attractive for ...

0 Tanujay Saha, et al. ∙

research

∙ 12/07/2022

CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework

Recently, automated co-design of machine learning (ML) models and accele...

0 Shikhar Tuli, et al. ∙

research

∙ 08/17/2022

CTRL: Clustering Training Losses for Label Error Detection

In supervised machine learning, use of correct labels is extremely impor...

0 Chang Yue, et al. ∙

research

∙ 07/09/2022

SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare

The Synthetic Control method has pioneered a class of powerful data-driv...

0 Bhishma Dedhia, et al. ∙

research

∙ 05/23/2022

FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?

The existence of a plethora of language models makes the problem of sele...

0 Shikhar Tuli, et al. ∙

research

∙ 08/07/2021

Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

The core network architecture of telecommunication systems has undergone...

0 Tanujay Saha, et al. ∙

research

∙ 05/31/2021

GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security

Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist o...

0 Jacob Brown, et al. ∙

research

∙ 04/05/2021

Fast Design Space Exploration of Nonlinear Systems: Part II

Nonlinear system design is often a multi-objective optimization problem ...

8 Prerit Terway, et al. ∙

research

∙ 04/05/2021

Fast Design Space Exploration of Nonlinear Systems: Part I

System design tools are often only available as blackboxes with complex ...

6 Sanjai Narain, et al. ∙

research

∙ 02/20/2021

MHDeep: Mental Health Disorder Detection System based on Body-Area and Deep Neural Networks

Mental health problems impact quality of life of millions of people arou...

0 Shayan Hassantabar, et al. ∙

research

∙ 01/07/2021

SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning

Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are in...

23 Tanujay Saha, et al. ∙

research

∙ 10/19/2020

Robot Design With Neural Networks, MILP Solvers and Active Learning

Central to the design of many robot systems and their controllers is sol...

0 Sanjai Narain, et al. ∙

research

∙ 10/12/2020

TUTOR: Training Neural Networks Using Decision Rules as Model Priors

The human brain has the ability to carry out new tasks with limited expe...

16 Shayan Hassantabar, et al. ∙

research

∙ 09/21/2020

DISPATCH: Design Space Exploration of Cyber-Physical Systems

Design of cyber-physical systems (CPSs) is a challenging task that invol...

5 Prerit Terway, et al. ∙

research

∙ 07/29/2020

Fully Dynamic Inference with Deep Neural Networks

Modern deep neural networks are powerful and widely applicable models th...

0 Wenhan Xia, et al. ∙

research

∙ 07/20/2020

CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

The novel coronavirus (SARS-CoV-2) has led to a pandemic. Due to its hig...

0 Shayan Hassantabar, et al. ∙

research

∙ 04/18/2020

Efficient Synthesis of Compact Deep Neural Networks

Deep neural networks (DNNs) have been deployed in myriad machine learnin...

0 Wenhan Xia, et al. ∙

research

∙ 12/18/2019

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

We introduce DeepInversion, a new method for synthesizing images from th...

27 Hongxu Yin, et al. ∙

research

∙ 12/12/2019

STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods

Neural networks (NNs) have been successfully deployed in many applicatio...

0 Shayan Hassantabar, et al. ∙

research

∙ 10/11/2019

DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks

Diabetes impacts the quality of life of millions of people. However, dia...

13 Hongxu Yin, et al. ∙

research

∙ 09/02/2019

SPRING: A Sparsity-Aware Reduced-Precision Monolithic 3D CNN Accelerator Architecture for Training and Inference

CNNs outperform traditional machine learning algorithms across a wide ra...

0 Ye Yu, et al. ∙

research

∙ 05/29/2019

SECRET: Semantically Enhanced Classification of Real-world Tasks

Supervised machine learning (ML) algorithms are aimed at maximizing clas...

0 Ayten Ozge Akmandor, et al. ∙

research

∙ 05/27/2019

Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks

Deep neural networks (DNNs) have become a widely deployed model for nume...

0 Xiaoliang Dai, et al. ∙

research

∙ 04/19/2019

SCANN: Synthesis of Compact and Accurate Neural Networks

Artificial neural networks (ANNs) have become the driving force behind r...

0 Shayan Hassantabar, et al. ∙

research

∙ 03/18/2019

Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture

Deep neural networks (DNNs) have been shown to outperform conventional m...

10 Ye Yu, et al. ∙

research

∙ 01/30/2019

Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM

Many long short-term memory (LSTM) applications need fast yet compact mo...

0 Hongxu Yin, et al. ∙

research

∙ 12/21/2018

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

This paper proposes an efficient neural network (NN) architecture design...

0 Xiaoliang Dai, et al. ∙

research

∙ 05/30/2018

Grow and Prune Compact, Fast, and Accurate LSTMs

Long short-term memory (LSTM) has been widely used for sequential data m...

0 Xiaoliang Dai, et al. ∙

research

∙ 05/30/2018

Grow and Prune Compact, Fast, and AccurateLSTMs

Long short-term memory (LSTM) has been widely used for sequential data m...

0 Xiaoliang Dai, et al. ∙

research

∙ 11/06/2017

NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm

Neural networks (NNs) have begun to have a pervasive impact on various a...

0 Xiaoliang Dai, et al. ∙

Niraj K. Jha

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