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Muhammad Shafique

4 papers hereh-index 6348 citations27 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AR2
  • cs.LG1
  • cs.NE1
same name
  • Muhammad Shafique — 55 papers, h 13
  • Muhammad Shafique — 19 papers, h 10
  • Muhammad Shafique — 14 papers, h 4
  • Muhammad Shafique — 9 papers, h 3
  • Muhammad Shafique — 9 papers, h 6
  • Muhammad Shafique — 9 papers, h 22

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators

4 papers

cs.AR2026

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference

Muhammad Usman, Muhammad Akmal Shafique, Shujaat Khan +1

Vision Transformers have reshaped computer vision by using self-attention to capture global context across image regions. This makes them attractive for edge visual inspection and…

cs.AR2025

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array

Abdul Rahoof, Vivek Chaturvedi, Mahesh Raveendranatha Panicker +1

In recent years, there has been a growing trend in accelerating computationally complex non-real-time beamforming algorithms in ultrasound imaging using deep learning models. Howev…

cs.NE2025

Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications

Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad +2

The challenging deployment of compute- and memory-intensive methods from Deep Neural Network (DNN)-based Continual Learning (CL) underscores the critical need for a paradigm shift…

cs.LG2024

Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges

Minghao Shao, Abdul Basit, Ramesh Karri +1

Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. Thes…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.