A Composable Design Space Exploration Framework to Optimize Behavioral Locking

L. Collini, R. Karri, C. Pilato
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引用次数: 4

Abstract

Globalization of the integrated circuit (IC) supply chain exposes designs to security threats such as reverse engineering and intellectual property (IP) theft. Designers may want to protect specific high-level synthesis (HLS) optimizations or micro-architectural solutions of their designs. Hence, protecting the IP of ICs is essential. Behavioral locking is an approach to thwart these threats by operating at high levels of abstraction instead of reasoning on the circuit structure. Like any security protection, behavioral locking requires additional area. Existing locking techniques have a different impact on security and overhead, but they do not explore the effects of alternatives when making locking decisions. We develop a design-space exploration (DSE) framework to optimize behavioral locking for a given security metric. For instance, we optimize differential entropy under area or key-bit constraints. We define a set of heuristics to score each locking point by analyzing the system dependence graph of the design. The solution yields better results for 92% of the cases when compared to baseline, state-of-the-art (SOTA) techniques. The approach has results comparable to evolutionary DSE while requiring 100× to 400× less computational time.
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优化行为锁定的可组合设计空间探索框架
集成电路(IC)供应链的全球化使设计面临逆向工程和知识产权(IP)盗窃等安全威胁。设计人员可能希望保护其设计的特定高级综合(HLS)优化或微体系结构解决方案。因此,保护集成电路的知识产权至关重要。行为锁定是一种阻止这些威胁的方法,它在高层次的抽象上操作,而不是在电路结构上进行推理。与任何安全保护一样,行为锁定需要额外的区域。现有的锁定技术对安全性和开销有不同的影响,但是在做出锁定决策时,它们没有研究备选方案的影响。我们开发了一个设计空间探索(DSE)框架来优化给定安全度量的行为锁定。例如,我们在面积或密钥位约束下优化微分熵。通过分析设计的系统依赖图,定义了一组启发式算法对每个锁定点进行评分。与最先进的基线(SOTA)技术相比,该解决方案在92%的病例中产生了更好的结果。该方法的结果与进化式DSE相当,而所需的计算时间减少了100到400倍。
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