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DAC2020顶会

DECOY: DEflection-Driven HLS-Based Computation Partitioning for Obfuscating Intellectual PropertY

Jianqi Chen, Monir Zaman, Yiorgos Makris, R. D. Shawn Blanton, Subhasish Mitra, Benjamin Carrión Schäfer

2020年份
28被引次数
3顶会引用

摘要

Among various competing designs targeting similar functionality, the key differentiator typically consists of a small amount of custom Intellectual Property (IP). To protect this IP from reverse engineering, designers need effective solutions for hiding the unique aspects of their implementations. In this work, we introduce a general framework for partitioning the computation performed by a design into a part whose implementation is commonly known (and encountered across many designs), and a part which is unique to this design. The former can then be built using conventional techniques (including untrusted manufacturing facilities) while the latter needs to be protected using additional obfuscation techniques. The existence of several other known implementations of the (same or similar) target function serves as a decoy which deflects efforts seeking to reverse-engineer the unique implementation. We demonstrate our framework using a hardware accelerator case study where (a) partitioning is performed through High Level Synthesis (HLS), (b) the commonly known portion of the accelerator is implemented as an Application Specific Integrated Circuit (ASIC), and (c) the unique portion of the accelerator is implemented on an embedded Field-Programmable Gate Array (eFPGA).

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