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Frank K. Gurkaynak

Researcher at ETH Zurich

Publications -  87
Citations -  2248

Frank K. Gurkaynak is an academic researcher from ETH Zurich. The author has contributed to research in topics: Encryption & Field-programmable gate array. The author has an hindex of 23, co-authored 83 publications receiving 1968 citations. Previous affiliations of Frank K. Gurkaynak include École Polytechnique Fédérale de Lausanne.

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Journal ArticleDOI

Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

TL;DR: In this paper, the authors describe the design of an open-source RISC-V processor core specifically designed for near-threshold (NT) operation in tightly coupled multicore clusters and introduce instruction extensions and micro-architectural optimizations to increase the computational density and to minimize the pressure toward the shared-memory hierarchy.
Proceedings ArticleDOI

Power-analysis attack on an ASIC AES implementation

TL;DR: This work presents the first results on the feasibility of power analysis attack against an AES hardware implementation and shows how to build a reliable measurement setup and how to improve the correlation coefficients, i.e., the signal to noise ratio for the authors' measurements.
Journal ArticleDOI

Globally Asynchronous, Locally Synchronous Circuits: Overview and Outlook

TL;DR: A pragmatic survey on the state of the art in GALS architectural techniques, design flows, and applications and several industrial inventions and changes in methodology, tools, and design flow that would improve GALS-based integration of IP blocks are provided.
Journal ArticleDOI

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

TL;DR: Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks is proposed.
Proceedings ArticleDOI

Efficient ASIC implementation of a real-time depth mapping stereo vision system

TL;DR: This paper presents a fast and area-efficient implementation of a real-time stereo vision algorithm for spatial depth mapping that combines two well-known area-based approaches to stereo matching and includes an occlusion detection method.