Conference Information
GLSVLSI 2022: Great Lakes Symposium on VLSI
Submission Date:
2022-02-15 Extended
Notification Date:
Conference Date:
Orange County, California, USA
CCF: c   QUALIS: b1   Viewed: 19117   Tracked: 30   Attend: 6

Call For Papers
Program Tracks

    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, CAD tools for biology and biomedical systems, 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, machine-learning for design and optimization of analog/digital/mixed-signal circuits, clock and power network optimization through applied machine-learning.
    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, reinenforcement learning, 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, TPU, educational techniques including novel curricula and laboratories, assessment methods, distance learning, textbooks, and design projects, Industry and academic collaborative programs and teaching.
Last updated by Dou Sun in 2022-02-01
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