Conference Information
GLSVLSI 2020: Great Lakes Symposium on VLSI
Submission Date:
2019-12-31 Extended
Notification Date:
Conference Date:
Beijing, China
CCF: c   QUALIS: b1   Viewed: 16116   Tracked: 27   Attend: 6
Conference Location
Call For Papers
 The 30th edition of the ACM Great Lakes Symposium on VLSI (GLSVLSI) will be held in Beijing, China. Original, unpublished papers describing research in the general areas of VLSI and hardware design are solicited. Stay tuned for more information.

In addition to the traditional topic areas of GLSVLSI listed below, papers are solicited for a special theme of “In-Memory Processing for Future Electronics”. 

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, 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.
Last updated by Dou Sun in 2020-03-11
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