Manoj Rohit Vemparala

According to our database1, Manoj Rohit Vemparala authored at least 26 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Wino Vidi Vici: Conquering Numerical Instability of 8-bit Winograd Convolution for Accurate Inference Acceleration on Edge.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

MATAR: Multi-Quantization-Aware Training for Accurate and Fast Hardware Retargeting.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2024

Pruning as a Binarization Technique.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Hardware Aware Robust Compression of Neural Networks.
PhD thesis, 2023

Lightweight Instruction Set for Flexible Dilated Convolutions and Mixed-Precision Operands.
Proceedings of the 24th International Symposium on Quality Electronic Design, 2023

WinoTrain: Winograd-Aware Training for Accurate Full 8-bit Convolution Acceleration.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

2022
HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology.
Leibniz Trans. Embed. Syst., 2022

Hardware Execution Time Prediction for Neural Network Layers.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms.
Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 2022

Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs.
Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 2022

Accelerating and pruning CNNs for semantic segmentation on FPGA.
Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

2021
HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology.
ACM Trans. Embed. Comput. Syst., 2021

BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices.
CoRR, 2021

Pruning CNNs for LiDAR-based Perception in Resource Constrained Environments.
Proceedings of the IEEE Intelligent Vehicles Symposium Workshops, 2021

Investigating Binary Neural Networks for Traffic Sign Detection and Recognition.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2021

BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops, 2021

BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks.
Proceedings of the Intelligent Systems and Applications, 2021

Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Adversarial Robust Model Compression Using In-Train Pruning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

Hardware-Aware Mixed-Precision Neural Networks using In-Train Quantization.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021

2020
Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

OrthrusPE: Runtime Reconfigurable Processing Elements for Binary Neural Networks.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks.
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

L2PF - Learning to Prune Faster.
Proceedings of the Computer Vision and Image Processing - 5th International Conference, 2020

2019
DSC: Dense-Sparse Convolution for Vectorized Inference of Convolutional Neural Networks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

An Efficient FPGA Accelerator Design for Optimized CNNs Using OpenCL.
Proceedings of the Architecture of Computing Systems - ARCS 2019, 2019


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