FPGAs are used in a wide variety of digital systems. Due to their ability to support parallelism and specialization, these devices are becoming more commonplace in fields such as machine learning. One of the biggest benefits of FPGAs, logic specialization, can lead to security risks. Prior research has shown that a large variety of malicious circuits can snoop on sensitive user data, induce circuit faults, or physically damage the FPGA. These Trojan circuits can easily be crafted and embedded in FPGA designs. Often, these Trojans are small, consume little power in comparison to the target circuit, and are hard to detect via simulation or physical inspection. Computer-aided design (CAD) software in FPGAs has been the subject of extensive research and development of FPGAs for the past thirty-five years. The current FPGA software landscape includes vendors that provide widely used software flows to convert behavioral and register-transfer level (RTL) descriptions to bitstreams needed to program an FPGA device. Given the complexity of the algorithms needed to perform this translation, these CAD tool flows are generally structured as black boxes with limited transparency regarding design conversion steps or the logical equivalence of the generated design and initial design specification. vi This work explores the enhancement of open-source FPGA software, SymbiFlow, that focuses on FPGA RTL synthesis, place and route and bitstream generation. SymbiFlow uses Yosys for synthesis, VPR for place and route, and Project X-Ray for bitstream generation. We focus on synthesis using Yosys and formal verification using the Cadence Conformal Logic Equivalence Checker (LEC) for Xilinx Artix-7 FPGAs. Yosys is used to synthesize 160 benchmarks written in Verilog. We implement required code modifications to Yosys for designs to pass the equivalence checker. For Conformal, this work involves processing 160 benchmark designs with the equivalence checker. Parameters can be toggled on or off to obtain results that indicates if a design has passed formal verification when comparing RTL and synthesized netlists.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-2307 |
| Date | 28 October 2022 |
| Creators | Muttur, Anurag V |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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