Predictive Model Attack for Embedded FPGA Logic Locking

被引:5
作者
Chowdhury, Prattay [1 ]
Sathe, Chaitali G. [1 ]
Schafer, Benjamin Carrion [1 ]
机构
[1] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75083 USA
来源
2022 ACM/IEEE INTERNATIONAL SYMPOSIUM ON LOW POWER ELECTRONICS AND DESIGN, ISLPED 2022 | 2022年
关键词
Hardware Security; Logic Locking; Hardware Redaction; FPGA; High-Level Synthesis; Machine Learning; OBFUSCATION;
D O I
10.1145/3531437.3539728
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With most VLSI design companies now being fabless it is imperative to develop methods to protect their Intellectual Property (IP). One approach that has become very popular due to its relative simplicity and practicality is logic locking. One of the problems with traditional locking mechanisms is that the locking circuitry is built into the netlist that the VLSI design company delivers to the foundry which has now access to the entire design including the locking mechanism. This implies that they could potentially tamper with this circuitry or reverse engineer it to obtain the locking key. One relatively new approach that has been coined logic locking through omission, or hardware redaction, maps a portion of the design to an embedded FPGA (eFPGA). The bitstream of the eFPGA now acts as the locking key. This new approach has been shown to be more secure as the foundry has no access to the bitstream during the manufacturing stage. The obvious drawbacks are the increase in design complexity and the area and performance overheads associated with the eFPGA. In this work we propose, to the best of our knowledge, the first attack on these type of new locking mechanisms by substituting the exact logic mapped onto the eFPGA by a synthesizable predictive model that replicates the behavior of the exact logic. We show that this approach is applicable in the context of approximate computing where hardware accelerators tolerate certain degree of errors at their outputs. Experimental results show that our proposed approach is very effective finding suitable predictive models while simultaneously reducing the overall power consumption.
引用
收藏
页数:6
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