J
Joonho Kong
Researcher at Kyungpook National University
Publications - 53
Citations - 714
Joonho Kong is an academic researcher from Kyungpook National University. The author has contributed to research in topics: Cache & Computer science. The author has an hindex of 11, co-authored 48 publications receiving 548 citations. Previous affiliations of Joonho Kong include Rice University & Korea University.
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Journal ArticleDOI
Recent thermal management techniques for microprocessors
TL;DR: The overall objective of this survey is to give microprocessor designers a broad perspective on various aspects of designing thermal-aware microprocessors and to guide future thermal management studies.
Proceedings ArticleDOI
PUFatt: Embedded Platform Attestation Based on Novel Processor-Based PUFs
Joonho Kong,Farinaz Koushanfar,Praveen Kumar Pendyala,Ahmad-Reza Sadeghi,Christian Wachsmann +4 more
TL;DR: PUFatt is presented, a new automatable method for linking software-based attestation to intrinsic device characteristics by means of a novel processor-based Physically Unclonable Function, which enables secure timed (and even) remote attestation particularly suitable for embedded and low-cost devices.
Proceedings ArticleDOI
Selective wordline voltage boosting for caches to manage yield under process variations
TL;DR: This work presents an analysis of a representative high-performance processor architecture and shows that the caches have the highest probability of causing yield losses under process variations, and proposes a novel selective wordline voltage boosting mechanism that aims at reducing the latency of the cache lines that are affected by process variations.
Journal ArticleDOI
Artificial Intelligence and Data Fusion at the Edge
TL;DR: In this paper, the authors proposed a framework for data fusion and AI processing at the edge and compared different levels of fusion and different types of artificial intelligence (AI) models and architectures.
Journal ArticleDOI
Enhancing online power estimation accuracy for smartphones
TL;DR: This paper proposes an advanced online power estimation technique for multi-core smartphones that models each hardware component's power behavior, considering the component’s power consumption characteristics.