Hasib-Al Rashid

Orcid: 0000-0002-9983-6929

According to our database1, Hasib-Al Rashid authored at least 14 papers between 2020 and 2024.

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

Timeline

Legend:

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Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
TinyM<sup>2</sup>Net-V2: A Compact Low-power Software Hardware Architecture for Multimodal Deep Neural Networks.
ACM Trans. Embed. Comput. Syst., May, 2024

TinyM<sup>2</sup>Net-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment.
CoRR, 2024

TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices.
CoRR, 2024

2023
HAC-POCD: Hardware-Aware Compressed Activity Monitoring and Fall Detector Edge POC Devices.
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2023

2022
E2HRL: An Energy-efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning.
ACM Trans. Design Autom. Electr. Syst., 2022

Automatic Detection of Respiratory Symptoms Using a Low-Power Multi-Input CNN Processor.
IEEE Des. Test, 2022

TinyM<sup>2</sup>Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices.
CoRR, 2022

2021
A Flexible Multichannel EEG Artifact Identification Processor using Depthwise-Separable Convolutional Neural Networks.
ACM J. Emerg. Technol. Comput. Syst., 2021

A Survey on the Optimization of Neural Network Accelerators for Micro-AI On-Device Inference.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2021

CoughNet: A Flexible Low Power CNN-LSTM Processor for Cough Sound Detection.
Proceedings of the 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2021

2020
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information.
CoRR, 2020

End-to-end Scalable and Low Power Multi-modal CNN for Respiratory-related Symptoms Detection.
Proceedings of the 33rd IEEE International System-on-Chip Conference, 2020

An Energy-Efficient Low Power LSTM Processor for Human Activity Monitoring.
Proceedings of the 33rd IEEE International System-on-Chip Conference, 2020

A Low-Power LSTM Processor for Multi-Channel Brain EEG Artifact Detection.
Proceedings of the 21st International Symposium on Quality Electronic Design, 2020


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