GLSVLSI 2020 : Great Lakes Symposium on VLSI

in Conferences   Posted on October 30, 2020 

Conference Information

Submission Deadline Tuesday 31 Dec 2019 Proceedings indexed by :
Conference Dates Sep 8, 2020 - Sep 11, 2020
Conference Address Online, Online
Conference & Submission Link https://www.glsvlsi.org/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 3.3
#Contributing Top Scientists 41
#Papers published by Top Scientists 65
Google Scholar H5-index 21
Guide2Research Overall Ranking: 213
Category Rankings
Hardware, Robotics & Electronics 37
Proceedings https://dl.acm.org/conference/glsvlsi

Conference Call for Papers

VLSI Design: ASIC and FPGA design, microprocessors/micro-architectures, embedded processors, analog/digital/mixed-signal systems, NoC, SoC, IoT, interconnects, memories, bio-inspired and neuromorphic circuits and systems, BioMEMs, lab-on-a-chip, biosensors, implantable and wearable devices.
VLSI Circuits and Power Aware Design: analog/digital/mixed-signal circuits, RF and communication circuits, chaos/neural/fuzzy-logic circuits, high-speed/low-power circuits, temperature estimation/optimization, power estimation/optimization.
Computer-Aided Design (CAD): hardware/software co-design, high-level synthesis, logic synthesis, simulation and formal verification, layout, design for manufacturing, CAD tools for biology and biomedical systems, algorithms and complexity analysis.
Testing, Reliability, Fault-Tolerance: digital/analog/mixed-signal testing, reliability, robustness, static and dynamic defect- and fault-recoverability, variation-aware design.
Emerging Computing & Post-CMOS Technologies: nanotechnology, molecular and quantum computing, approximate and stochastic computing, sensor and sensor networks, post CMOS VLSI.
Hardware Security: trusted IC, IP protection, hardware security primitives, reverse engineering, hardware Trojan, side-channel analysis, CPS and IoT security.
VLSI for Machine Learning and Artificial Intelligence: hardware accelerators for machine learning, computer architectures for machine learning, deep learning, brain-inspired computing, big data computing, cloud computing for Internet-of-Things (IoT) devices.
Microelectronic Systems Education: pedagogical innovations using a wide range of technologies such as ASIC, FPGA, multicore, GPU, educational techniques including novel curricula and laboratories, assessment methods, distance learning, textbooks, and design projects, Industry and academic collaborative programs and teaching.

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