This paper presents a high-level circuit obfuscation technique to prevent the theft of intellectual property (IP) of integrated circuits. In particular, our technique protects a class of circuits that relies on constant multiplications, such as filters and neural networks, where the constants themselves are the IP to be protected. By making use of decoy constants and a key-based scheme, a reverse engineer adversary at an untrusted foundry is rendered incapable of discerning true constants from decoy constants. The time-multiplexed constant multiplication (TMCM) block of such circuits, which realizes the multiplication of an input variable by a constant at a time, is considered as our case study for obfuscation. Furthermore, two TMCM design architectures are taken into account; an implementation using a multiplier and a multiplierless shift-adds implementation. Optimization methods are also applied to reduce the hardware complexity of these architectures. The well-known satisfiability (SAT) and automatic test pattern generation (ATPG) attacks are used to determine the vulnerability of the obfuscated designs. It is observed that the proposed technique incurs small overheads in area, power, and delay that are comparable to the hardware complexity of prominent logic locking methods. Yet, the advantage of our approach is in the insight that constants -- instead of arbitrary circuit nodes -- become key-protected.
翻译:本文展示了防止综合电路知识产权被盗的高级电路迷惑技术。 特别是, 我们的技术保护了一类依赖不断倍增的电路, 如过滤器和神经网络, 常数本身是需要保护的 IP 。 通过使用诱饵常数和一个基于关键的方法, 一个不可信的铸造厂的反向工程对手无法辨别诱饵常数的真实常数 。 这种电路的多时重复常数(TMCM) 块( TMCM), 它通过一个常数在一个时数中实现输入变量的倍增, 被视为我们对难以解析的案例研究。 此外, 考虑了两个TMCM设计结构; 使用一个乘数常数常数常数常数和无倍数的变换加法实施。 优化方法还被用来降低这些结构的硬件复杂性。 众所周知的可反常数和自动测试模式生成( ATGPG) 攻击被用来确定一个输入变量变量变数的易变数, 被视为我们无法解变的直径的直径方法。 它被观察到了, 核心的逻辑的复杂度方法成为了。 。