Tendayi Kamucheka
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Faculty Researcher
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Biography and Research Information
OverviewAI-generated summary
Tendayi Kamucheka's research focuses on the design and implementation of hardware accelerators for deep learning and post-quantum cryptography (PQC) algorithms. His work explores in-memory computing architectures, such as the IMAGine and DA-VinCi overlays, which aim to enhance computational efficiency by performing operations directly within memory units. Kamucheka also investigates security aspects of PQC, including power-based side-channel attack analysis and hardware implementations of algorithms like Kyber. He has collaborated extensively with researchers at the University of Arkansas at Fayetteville, including Miaoqing Huang and David Andrews, contributing to a shared publication record. Kamucheka's recent publications include work on compiler-driven approaches for hardware-software co-design of deep-learning accelerators and benchmarks for field simulations of microstrip patch antennas.
Metrics
- h-index: 3
- Publications: 10
- Citations: 43
Selected Publications
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DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing (2025)
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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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A Masked Pure-Hardware Implementation of Kyber Cryptographic Algorithm (2022)
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Benchmark of Acceleware vs XFdtd for Field Simulations of Microstrip Patch Antenna (2020)
Collaboration Network
Top Collaborators
- A Masked Pure-Hardware Implementation of Kyber Cryptographic Algorithm
- IMAGine: An In-Memory Accelerated GEMV Engine Overlay
- The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators
- DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing
- Ph.D. Project: A Compiler-Driven Approach to HW/SW Co-Design of Deep-Learning Accelerators
- A Masked Pure-Hardware Implementation of Kyber Cryptographic Algorithm
- IMAGine: An In-Memory Accelerated GEMV Engine Overlay
- The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators
- DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing
- IMAGine: An In-Memory Accelerated GEMV Engine Overlay
- The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators
- DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing
- IMAGine: An In-Memory Accelerated GEMV Engine Overlay
- The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators
- DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing
- IMAGine: An In-Memory Accelerated GEMV Engine Overlay
- The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators
- IMAGine: An In-Memory Accelerated GEMV Engine Overlay
- The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators
- A Masked Pure-Hardware Implementation of Kyber Cryptographic Algorithm
- DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing
- DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing
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