Search Results for author: Syed Rafay Hasan

Found 13 papers, 0 papers with code

System Integration of Xilinx DPU and HDMI for Real-Time inference in PYNQ Environment with Image Enhancement

no code implementations15 Dec 2023 Jonathan Sanderson, Syed Rafay Hasan

To validate our proposed methodology, LEAP, a simple image enhancement algorithm, histogram equalization, is designed and integrated in the FPGA fabric along with Xilinx's Deep Processing Unit (DPU).

Edge-computing Image Enhancement

A Case Study of Image Enhancement Algorithms' Effectiveness of Improving Neural Networks' Performance on Adverse Images

no code implementations15 Dec 2023 Jonathan Sanderson, Syed Rafay Hasan

To fill this knowledge gap, we provide a case study on two popular image enhancement algorithms, Histogram Equalization (HE) and Retinex (RX).

Autonomous Vehicles Image Enhancement

Towards Enabling Dynamic Convolution Neural Network Inference for Edge Intelligence

no code implementations18 Feb 2022 Adewale Adeyemo, Travis Sandefur, Tolulope A. Odetola, Syed Rafay Hasan

We further propose a library-based approach to design scalable and dynamic distributed CNN inference on the fly leveraging partial-reconfiguration techniques, which is particularly suitable for resource-constrained edge devices.

Security Analysis of Capsule Network Inference using Horizontal Collaboration

no code implementations22 Sep 2021 Adewale Adeyemo, Faiq Khalid, Tolulope A. Odetola, Syed Rafay Hasan

Similar to traditional CNNs, CapsNet is also vulnerable to several malicious attacks, as studied by several researchers in the literature.

Collaborative Inference Self-Driving Cars

Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things

no code implementations13 Jul 2021 Hawzhin Mohammed, Tolulope A. Odetola, Nan Guo, Syed Rafay Hasan

In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices.

FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack

no code implementations13 Jun 2021 Tolulope Odetola, Faiq Khalid, Travis Sandefur, Hawzhin Mohammed, Syed Rafay Hasan

Since in horizontal collaboration of RC AIoT devices different sections of CNN architectures are outsourced to different untrusted third parties, the attacker may not know the input image, but it has access to the layer-by-layer output feature maps information for the assigned sections of the CNN architecture.

SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)

no code implementations16 Mar 2021 Tolulope A. Odetola, Syed Rafay Hasan

Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e. g. low end FPGAs) is a growing research area.

MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices

no code implementations21 Nov 2020 Faiq Khalid, Syed Rafay Hasan, Sara Zia, Osman Hasan, Falah Awwad, Muhammad Shafique

To reduce the overhead of data acquisition, we propose a single power-port current acquisition block using current sensors in time-division multiplexing, which increases accuracy while incurring reduced area overhead.

BIG-bench Machine Learning

How Secure is Distributed Convolutional Neural Network on IoT Edge Devices?

no code implementations16 Jun 2020 Hawzhin Mohammed, Tolulope A. Odetola, Syed Rafay Hasan

The deployment of CNN on resource-constrained edge devices have proved challenging.

2L-3W: 2-Level 3-Way Hardware-Software Co-Verification for the Mapping of Deep Learning Architecture (DLA) onto FPGA Boards

no code implementations14 Nov 2019 Tolulope A. Odetola, Katie M. Groves, Syed Rafay Hasan

To the best of our knowledge this is the first work that proposes a 2-Level 3-Way (2L-3W) hardware-software co-verification methodology and provides a step-by-step guide for the successful mapping, deployment and verification of DLA on FPGA boards.

A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices

no code implementations5 Nov 2019 Tolulope A. Odetola, Ogheneuriri Oderhohwo, Syed Rafay Hasan

In this paper, we propose a methodology that solves this problem by extending the capability of existing multi-label classification and provide models with lower latency that requires smaller memory size when deployed on edge devices.

Classification General Classification +3

SIMCom: Statistical Sniffing of Inter-Module Communications for Run-time Hardware Trojan Detection

no code implementations4 Nov 2018 Faiq Khalid, Syed Rafay Hasan, Osman Hasan, Muhammad Shafique

We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC), named as SIMCom.

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