The 34^th edition of GLSVLSI will be held as an in-person conference. Original, unpublished papers describing research in the general areas of VLSI and hardware design are solicited. Please visit http://www.glsvlsi.org/ for more information.

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, analog/digital/mixed-signal circuits.
• IoT and Smart Systems: circuits, computing, processing, and design of IoT and smart systems such as smart cities, smart healthcare, smart transportation, smart grid, cyber-physical systems, edge computing, machine learning for IoT, TinyML.
• 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, 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.