Research

My research path began with hardware security primitives and progressed to the system level. FPGA Virtualization is a key aspect of my Ph.D. research. My current research direction will focus more on the contribution of hardware systems to security and privacy. I will continue to explore truly valuable cloud computing systems from hardware, system, and software levels.


  1. FPGA Virtualization System
  2. Hardware Security
  3. Trustworthy AI Acceleration and Optimization

FPGA Virtualization System: Hardware-Oriented Threats and Mitigations

Technologies for cloud virtualization are extensively utilized in public cloud services. Currently, there is significant focus in both industry and academic research on virtualization technologies involving FPGA-based cloud instances.

Our vision is to develop a commercial prototype of a Virtualized FPGA, further enhancing hardware acceleration support for internet applications. Considering the distinct system architecture of cloud FPGA instances, as opposed to traditional CPU/GPU-based cloud instances, and the hardware programmability of FPGAs, our focus begins with addressing the security challenges of multi-tenant FPGAs. We explore potential attack models and specific, commonly used attack methods, such as those in AI inference applications, to generate ideas for the design of Cloud FPGA hypervisors.


Hardware Security

My main contributions in the field of hardware security are focused on the development of hardware security primitives.

  • Designed a True Random Number Generator (TRNG) hardware circuit based on Chaotic Cellular Automata Topology.
Conway's Game of Life


Trustworthy AI Acceleration and Optimization

The enactment of stricter privacy laws and the awakening of public awareness towards privacy protection demand higher levels of privacy and security for cloud services, urgently requiring the development of a reliable, secure computing accelerator. This research field aims to apply methods such as Multipart Computing (MPC), Fully Homomorphic Encryption (FHE), and Trusted Execution Environment (TEE) (e.g., Intel SGX, ARM TrustZone, AMD SEV) technologies to enable a trustworthy AI accelerator in the cloud, addressing critical privacy and security concerns.