Reverse Engineering Digital ICs through Geometric Embedding of Circuit Graphs

Author:

Cakir Burcin1,Malik Sharad1

Affiliation:

1. Princeton University, Princeton, NJ ,USA

Abstract

Outsourcing of design and manufacturing processes makes integrated circuits (ICs) vulnerable to adversarial changes and raises concerns about their integrity. Reverse engineering the manufactured netlist helps identify malicious insertions. In this article, we present an automated approach that, given a reference design description with high-level blocks, infers these blocks in an untrusted gate-level (test) implementation. Using the graph connectivity of the netlists, we compute a geometric embedding for each wire in the circuits, which, then, is used to compute a bipartite matching between the nodes of the two designs and identify high-level blocks in the test circuit. Experiments to evaluate the efficacy of the proposed technique on various-sized designs, including the multi-core processor OpenSparc T1, show that it can correctly match over 90% of gates in the test circuit to their corresponding block in the reference model.

Funder

C-FAR

MARCO and DARPA

one of the six SRC STARnet Centers

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference35 articles.

1. {n. d.}. OpenCores 2016. Digital Open Source Hardware Cores. Retrieved August 2016 from http://opencores.org/. {n. d.}. OpenCores 2016. Digital Open Source Hardware Cores. Retrieved August 2016 from http://opencores.org/.

2. {n. d.}. OpenSparc T1 2016. Open Source Multi-core Processor. Retrieved August 2016 from http://oracle.com/technetwork/systems/opensparc/opensparc-t1-page-1444609.html. {n. d.}. OpenSparc T1 2016. Open Source Multi-core Processor. Retrieved August 2016 from http://oracle.com/technetwork/systems/opensparc/opensparc-t1-page-1444609.html.

3. Geometric embeddings for faster and better multi-way netlist partitioning

4. Spectral partitioning with multiple eigenvectors

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