The goal of our research is to explore and build the next-generation hardware based on post-CMOS emerging devices, e.g. memristors. From top-down, we examine the existing algorithms (like Transformers) and determine the most crucial task to accelerate with our hardware. Examples include but not limited to matrix multiplication in A.I./machine learning tasks. In the meanwhile, we optimize and characterize the device performance, including their nonlinear dynamics, and build novel circuit/architecture/algorithm from bottom up.

The followings are example topics. If you are interested in joining us (as research postgraduate (e.g. Ph.D., MPhil.,) students, research assistant, postdoc) and would like to know about ongoing topics, please feel free to contact me at canl@hku.hk.

1. CMOS compatible novel devices and their integration/nanofabrication

We explore novel devices such as memristors (RRAM), atomically thin two-dimensional materials (like MoS2), and other novel device concepts, to build next-generation memory and computing devices with superior energy efficiency and scaling potential.

Devices

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2. Novel applications and their co-design.

Beyond standard AI, we develop hardware-software co-designs for specialized domain applications, such as real-time genomic analysis and solving hard optimization problems.

Apps

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3. A.I./Machine learning hardware accelerators and precision analog computing.

We build high-precision analog computing platforms and efficient AI accelerators, addressing key challenges like device non-ideality, precision, and peripheral circuit overhead (ADC).

Chip

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Funding Support

We gratefully acknowledge the support from the following agencies:

Croucher RGC ITC NSFC
Croucher Foundation Research Grants Council Inno & Tech Commission NSFC
Innovation Award TRS, CRF, GRF, JRS, ECS MHKJFS, InnoHK Excellent Young Scientists Fund