Arman Roohi

Assistant Professor

University of Nebraska - Lincoln

United States

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Arman Roohi is an Assistant Professor at the University of Nebraska-Lincoln in the United States. His research interests focus on low-power computing, neuro-symbolic AI, neuromorphic computing, and advanced processing techniques for vision sensors. Recent publications include studies on efficient edge device architectures and novel simulation tools for intelligent vision systems.

Articles (19)

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2020

Collaborators (6)

Mohsen Imani

Assistant Professor

University of California, Irvine

UNITED STATES

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Durgamadhab Misra

Professor

New Jersey Institute of Technology

UNITED STATES

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Nathaniel C Cady

UNITED STATES

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David Z. Pan

Professor

University of Texas at Austin

UNITED STATES

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Shaahin Angizi

Assistant Professor

New Jersey Institute of Technology

UNITED STATES

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Sepehr Tabrizchi

The University of Illinois at Chicago

UNITED STATES

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Arman Roohi

Research Interests

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About

Articles (19)

PINSim: A Processing In- and Near-Sensor Simulator to Model Intelligent Vision Sensors

Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate

WinEdge: Low-Power Winograd CNN Execution with Transposed MRAM for Edge Devices

Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence

Enabling Normally-Off In Situ Computing With a Magneto-Electric FET-Based SRAM Design

Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study

APRIS: Approximate Processing ReRAM In-Sensor Architecture Enabling Artificial-Intelligence-Powered Edge

ViTSen: Bridging Vision Transformers and Edge Computing With Advanced In/Near-Sensor Processing

A Near-Sensor Processing Accelerator for Approximate Local Binary Pattern Networks

AppCiP: Energy-Efficient Approximate Convolution-in-Pixel Scheme for Neural Network Acceleration

Design and Evaluation of a Near-Sensor Magneto-Electric FET-Based Event Detector

PISA: A Non-Volatile Processing-In-Sensor Accelerator for Imaging Systems

PiPSim: A Behavior-Level Modeling Tool for CNN Processing-in-Pixel Accelerators

Design and evaluation of ultra‐fast 8‐bit approximate multipliers using novel multicolumn inexact compressors

MR-PIPA: An Integrated Multilevel RRAM (HfO x )-Based Processing-In-Pixel Accelerator

LT-PIM: An LUT-Based Processing-in-DRAM Architecture With RowHammer Self-Tracking

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Ocelli: Efficient Processing-in-Pixel Array Enabling Edge Inference of Ternary Neural Networks

ApGAN: Approximate GAN for Robust Low Energy Learning From Imprecise Components

Collaborators (6)

Mohsen Imani

Durgamadhab Misra

Nathaniel C Cady

David Z. Pan

Shaahin Angizi

Sepehr Tabrizchi

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