Open Access Thesis
Electrical & Computer Engineering
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
Month Degree Awarded
With the importance of data security at its peak today, many reconfigurable systems are used to provide security. This protection is often provided by FPGA-based encrypt/decrypt cores secured with secret keys. Physical unclonable functions (PUFs) use random manufacturing variations to generate outputs that can be used in keys. These outputs are specific to a chip and can be used to create device-tied secret keys. Due to reliability issues with PUFs, key generation with PUFs typically requires error correction techniques. This can result in substantial hardware costs. Thus, the total cost of a $n$-bit key far exceeds just the cost of producing $n$ bits of PUF output. To tackle this problem, we propose the use of variation aware intra-FPGA PUF placement to reduce the area cost of PUF-based keys on FPGAs. We show that placing PUF instances according to the random variations of each chip instance reduces the bit error rate of the PUFs and the overall resources required to generate the key. Our approach has been demonstrated on a Xilinx Zynq-7000 programmable SoC using FPGA specific PUFs with code-offset error correction based on BCH codes. The approach is applicable to any PUF-based system implemented in reconfigurable logic. To evaluate our approach, we first analyze the key metrics of a PUF - reliability and uniqueness. Reliability is related to bit error rate, an important parameter with respect to error correction. In order to generate reliable results from the PUFs, a total of four ZedBoards containing FPGAs are used in our approach. We quantify the effectiveness of our approach by implementing the same key generation scheme using variation-aware and default placement, and show the resources saved by our approach.
Vyas, Shrikant S., "Variation Aware Placement for Efficient Key Generation using Physically Unclonable Functions in Reconfigurable Systems" (2016). Masters Theses. 452.