David Andrews Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
faculty
Research Areas
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Biography and Research Information
OverviewAI-generated summary
David Andrews' research focuses on the intersection of computer engineering, machine learning, and cybersecurity. He has published work on hardware implementations of cryptographic algorithms, including a masked pure-hardware implementation of the Kyber cryptographic algorithm and power-based side-channel attack analysis on post-quantum cryptography algorithms. His work also extends to machine learning applications, with publications on high-rate machine learning for forecasting time-series signals and accelerating LSTM-based high-rate dynamic system models. Andrews has also investigated the development of specialized hardware for machine learning, such as a customizable domain-specific memory-centric FPGA overlay.
His research group at the University of Arkansas at Fayetteville collaborates with faculty members such as Miaoqing Huang, Ehsan Kabir, and Tendayi Kamucheka. Andrews has received funding for his work, including a $100,000 NSF grant as Co-PI for research on infrastructure to perform side-channel attacks on cryptographic algorithms. His scholarship metrics include an h-index of 10, with 33 total publications and 388 total citations.
Metrics
- h-index: 23
- Publications: 201
- Citations: 2,162
Selected Publications
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DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing (2025)
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N-TORC: Native Tensor Optimizer for Real-Time Constraints (2025)
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Optimized Coding and Parameter Selection for Efficient FPGA Design of Attention Mechanisms (2025)
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Resource Scheduling for Real-Time Machine Learning (2025)
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Famous: Flexible Accelerator for the Attention Mechanism of Transformer on Ultrascale+ FPGAs (2024)
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ProTEA: Programmable Transformer Encoder Acceleration on FPGA (2024)
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IMAGine: An In-Memory Accelerated GEMV Engine Overlay (2024)
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The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators (2024)
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Ph.D. Project: A Compiler-Driven Approach to HW/SW Co-Design of Deep-Learning Accelerators (2024)
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Towards Cloud-based Infrastructure for Post-Quantum Cryptography Side-channel Attack Analysis (2023)
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FPGA Processor In Memory Architectures (PIMs): Overlay or Overhaul ? (2023)
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Accelerating LSTM-Based High-Rate Dynamic System Models (2023)
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FPGA Processor In Memory Architectures (PIMs): Overlay or Overhaul ? (2023)
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Making BRAMs Compute: Creating Scalable Computational Memory Fabric Overlays (2023)
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A Runtime Programmable Accelerator for Convolutional and Multilayer Perceptron Neural Networks on FPGA (2022)
Federal Grants 1 $100,000 total
Collaboration Network
Top Collaborators
- Power-based Side Channel Attack Analysis on PQC Algorithms.
- ProTEA: Programmable Transformer Encoder Acceleration on FPGA
- ProTEA: Programmable Transformer Encoder Acceleration on FPGA
- Optimized Coding and Parameter Selection for Efficient FPGA Design of Attention Mechanisms
- A runtime-adaptive transformer neural network accelerator on FPGAs
- ProTEA: Programmable Transformer Encoder Acceleration on FPGA
- ProTEA: Programmable Transformer Encoder Acceleration on FPGA
- Optimized Coding and Parameter Selection for Efficient FPGA Design of Attention Mechanisms
- A runtime-adaptive transformer neural network accelerator on FPGAs
- ProTEA: Programmable Transformer Encoder Acceleration on FPGA
- ProTEA: Programmable Transformer Encoder Acceleration on FPGA
- Optimized Coding and Parameter Selection for Efficient FPGA Design of Attention Mechanisms
- A runtime-adaptive transformer neural network accelerator on FPGAs
- Impact of changes in the frequency of food pantry utilization on client food security and well‐being
- Impact of changes in the frequency of food pantry utilization on client food security and well‐being
- Power-based Side Channel Attack Analysis on PQC Algorithms.
- Power-based Side Channel Attack Analysis on PQC Algorithms.
- Power-based Side Channel Attack Analysis on PQC Algorithms.
- Power-based Side Channel Attack Analysis on PQC Algorithms.
- Comer Schemes, Relation Algebras, and the Flexible Atom Conjecture
- Comer Schemes, Relation Algebras, and the Flexible Atom Conjecture
- Optimized Coding and Parameter Selection for Efficient FPGA Design of Attention Mechanisms
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