Research Topics

• Three-Dimensional (3D) Integrated Circuits
  With Moore's Law reaching limitations in transistor scaling, one solution is to expand vertically by stacking multiple silicon layers interconnected by vias. Our focus is on designing 3D architectures, such as memory stacks (SRAM, eDRAM, ReRAM, etc.), and converting conventional 2D architectures to 3D.

• Neuromorphic Computing
  Inspired by the human brain, we design neuromorphic chips featuring physical artificial neurons made of silicon. These chips perform computations that mimic biological brain functions. Our approach combines digital methods with 3D Integrated Circuits and Network-on-Chip (NoC) structures, developed in both hardware (Verilog HDL) and software (Python) models.

• Carbon-efficient & Sustainable Computing
  Computers, data centers, and devices use a lot of energy, often from fossil fuels, which release carbon and cause climate change. By making computing carbon neutral/efficient, we can protect the environment by cutting carbon emissions. Our carbon-efficient computing aims to designing and operating computing systems to minimize their carbon footprint, aiming for greater energy efficiency and reduced environmental impact.

• Undervolted/Near-Threshold Computing
  Reducing the operating voltage to near-threshold levels greatly improves carbon efficiency by lowering power consumption, which directly reduces carbon emissions. However, this also increases the risk of noise and errors, affecting system reliability. Our research focuses on exploring the impact of undervolting on chip performance and developing robust strategies to manage noise and errors, ensuring reliable operation while maximizing energy savings.

• Approximate Computing
  To minimize power consumption and area costs, we utilize approximate circuits that prioritize efficiency by delivering "good enough" results instead of precise calculations. This trade-off reduces energy use and chip size, which in turn lowers the carbon emissions associated with manufacturing and operating these devices. By focusing on efficient computation, we aim to contribute to a more sustainable and environmentally friendly technology landscape.

• Edge Intelligence for Disasters
  This topic leverages edge computing and AI to enable real-time disaster monitoring, prediction, and response. By processing data locally on IoT devices and low-power edge hardware, this approach reduces latency and ensures rapid decision-making even in areas with limited connectivity. Integrating edge intelligence into disaster management can significantly enhance preparedness and mitigate risks in vulnerable regions.