Conference Information
GLSVLSI 2023: Great Lakes Symposium on VLSI
Submission Date:
Notification Date:
Conference Date:
Knoxville, Tennessee, USA
CCF: c   QUALIS: b1   Viewed: 23296   Tracked: 33   Attend: 6

Call For Papers
Program Tracks

    VLSI Circuits and Design: ASIC and FPGA design, microprocessors/micro-architectures, embedded processors, high-speed/low-power circuits, analog/digital/mixed-signal systems, NoC, SoC, IoT, interconnects, memories, bio-inspired and neuromorphic circuits and systems, BioMEMs, lab-on-a-chip, biosensors, CAD tools for biology and biomedical systems, implantable and wearable devices, machine-learning for design and optimization of VLSI circuits and design.
    IoT and Smart Systems: Circuits, computing, processing, and design of IoT and smart systems such as smart cities, smart healthcare, smart transportation, smart grid etc.; cyber-physical systems, edge computing, machine learning for IoT, TinyML, cloud computing for IoT devices.
    Computer-Aided Design (CAD): Hardware/software co-design, high-level synthesis, logic synthesis, simulation and formal verification, layout, design for manufacturing, algorithms and complexity analysis, physical design (placement, route, CTS), static timing analysis, signal and power integrity, machine learning for CAD and EDA design.
    Testing, Reliability, Fault-Tolerance: Digital/analog/mixed-signal testing, reliability, robustness, static/dynamic defect- and fault-recoverability, variation-aware design, learning-assisted testing.
    Emerging Computing & Post-CMOS Technologies: Nanotechnology, 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 Trojans, side-channel analysis, CPS/IoT security, machine learning for HW security.
    VLSI for Machine Learning and Artificial Intelligence: Hardware accelerators for machine learning, novel architectures for deep learning, brain-inspired computing, big data computing, reinforcement learning.
    Microelectronic Systems Education: Pedagogical innovations using a wide range of technologies such as ASIC, FPGA, multicore, GPU, TPU, educational techniques including novel curricula and laboratories, assessment methods, distance learning, textbooks, design projects, and industry/academic collaborative programs and teaching.
Last updated by Dou Sun in 2022-12-12
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cIEEE Transactions on GamesIEEE2475-1502
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Full NameImpact FactorPublisher
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IEEE Transactions on GamesIEEE
Proceedings of the IEEE10.69IEEE
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IPSJ Transactions on System LSI Design Methodology Information Processing Society of Japan
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IEEE Transactions on ReliabilityIEEE