Associate Professor Cai Kui

Project Info

The surge of Big Data and artificial intelligence (AI) has created a tremendous demand for automated storage and retrieval of huge amounts of data. The data storage industry serves this need, and is one of the most dynamic industries in the world. In recent years, the non-volatile memories (NVMs), such as the flash memory used in the solid state drives (SSDs), have shown a great potential for future ultra-high density data storage systems.

The objective of the Ph.D’s work is to develop efficient channel coding algorithms and techniques to improve the various performance of NVMs, such as the recording density, the endurance and retention, the read/write access time, as well as the power consumption. The Ph.D work will focus on the design of novel and efficient channel codes for NVMs. Both error correction codes (ECCs) and constrained codes will be especially designed and optimized for the corresponding NVM channels. During the study, both theoretical analysis and computer simulations will be carried out, to determine the theoretical limit and to evaluate the performance gain of the designed codes.

The Ph.D work is designed to address the current and near future major technical challenges of the data storage industry, and hence the students will find plenty opportunities from both the industry and academia after graduation. The corresponding companies include (but not limited to) Broadcom, Qualcomm, Marvell, Western Digital (WD), Seagate, Hwawei, Intel, Micro, Samsung, Toshiba, etc.