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Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

IEEE access, 2020, Vol.8, p.225134-225180

2020

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An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks

Future internet, 2020-07, Vol.12 (7), p.113

2020

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Biobjective gradient descent for feature selection on high dimension, low sample size data

PloS one, 2024-07, Vol.19 (7), p.e0305654

2024

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An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach

PloS one, 2024-07, Vol.19 (7), p.e0305092

2024

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A blended wavefield separation method for seismic exploration based on improved GoogLeNet

PloS one, 2024-06, Vol.19 (6), p.e0304207

2024

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A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

PloS one, 2024-06, Vol.19 (6), p.e0303049

2024

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AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient

IEEE access, 2022, Vol.10, p.1-1

2022

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A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks

Information (Basel), 2023-10, Vol.14 (10), p.537

2023

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RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

IEEE access, 2022, Vol.10, p.1-1

2022

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CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reuse

2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019, p.964-967

2019

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IEEE_Xplore

DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware

IEEE transactions on computer-aided design of integrated circuits and systems, 2021-09, Vol.40 (9), p.1768-1781

2021

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IEEE_Xplore

ISMatch: A real-time hardware accelerator for inexact string matching of DNA sequences on FPGA

Microprocessors and microsystems, 2023-03, Vol.97, p.104763, Article 104763

2023

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FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators

IEEE transactions on very large scale integration (VLSI) systems, 2021-04, Vol.29 (4), p.716-729

2021

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IEEE_Xplore

LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, p.79-86

2022

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IEEE_Xplore

ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations

2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020, p.1205-1210

2020

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IEEE_Xplore

Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021, p.774-779

2021

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IEEE_Xplore

SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks

Microprocessors and microsystems, 2023-02, Vol.96, p.104738, Article 104738

2023

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NASCaps: a framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks

2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2020, p.1-9

2020

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IEEE_Xplore

R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, p.6315-6321

2021

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IEEE_Xplore

Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks

2020 57th ACM/IEEE Design Automation Conference (DAC), 2020, p.1-6

2020

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Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper

2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021, p.1-9

2021

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IEEE_Xplore

Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems

2022 IEEE 40th VLSI Test Symposium (VTS), 2022, p.1-14

2022

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IEEE_Xplore

Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges

2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2019, p.553-559

2019

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IEEE_Xplore

Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations

Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, 2022, p.1-6

2022

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MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021, p.108-113

2021

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IEEE_Xplore

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Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks

Biobjective gradient descent for feature selection on high dimension, low sample size data

An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach

A blended wavefield separation method for seismic exploration based on improved GoogLeNet

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient

A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks

RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reuse

DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware

ISMatch: A real-time hardware accelerator for inexact string matching of DNA sequences on FPGA

FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators

LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor

ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations

Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks

NASCaps: a framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks

R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors

Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks

Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper

Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems

Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges

Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations

MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

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