Highway to HAL

Wallat, Sebastian; Albartus, Nils; Becker, Steffen; Hoffmann, Max; Ender, Maik; Fyrbiak, Marc; Drees, Adrian; Maaßen, Sebastian; Paar, Christof

doi:10.1145/3310273.3323419

Cited by 19 publications

(3 citation statements)

References 17 publications

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“…Figure 4 shows the order in which the techniques are integrated. HAL [16] is used to parse the netlist. Then, the different carry-chains are identified in the netlist, and carrychain-based operations are mapped as part of a module as shown in blue.…”

Section: Tool-chain Workflowmentioning

confidence: 99%

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Reverse Engineering Word-Level Models from Look-Up Table Netlists

Narayanan¹,

Venkatesan²,

Pula³

et al. 2023

Preprint

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Reverse engineering of FPGA designs from bitstreams to RTL models aids in understanding the high level functionality of the design and for validating and reconstructing legacy designs. Fast carry-chains are commonly used in synthesis of operators in FPGA designs. We propose a method to detect word-level structures by analyzing these carry-chains in LUT (Look-Up Table) level netlists. We also present methods to adapt existing techniques to identify combinational operations and sequential modules in ASIC netlists to LUT netlists. All developed and adapted techniques are consolidated into an integrated tool-chain to aid in reverse engineering of word-level designs from LUT-level netlists. When evaluated on a set of real-world designs, the tool-chain infers 34% to 100% of the elements in the netlist to be part of a known word-level operation or a known sequential module.

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Section: Tool-chain Workflowmentioning

confidence: 99%

“…The techniques described in this paper were implemented using Python. HAL [16] was used to perform fundamental operations on the netlist such as graph traversal and storing and retrieving data on design elements. All the benchmark designs were first synthesized using Artix 7-series and Zync 7series FPGAs using Xilinx Vivado.…”

Section: Tool-chain Workflowmentioning

confidence: 99%

Reverse Engineering Word-Level Models from Look-Up Table Netlists

Narayanan¹,

Venkatesan²,

Pula³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It is of note that this effect was more pronounced for visual compared to verbal memory aids. Again, the use of visual aids is also mirrored in the work of professional troubleshooters, for example, hardware reverse engineers (Rematska & Bourbakis, 2016;Wallat et al, 2019).…”

Section: Visual Aids For Solving Ntpsmentioning

confidence: 99%

The impact of inducing troubleshooting strategies via visual aids on performance in a computerized digital network task

Bordewieck

Elson

2021

Applied Cognitive Psychology

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Summary Troubleshooting is a particular problem‐solving process comprising error detection, fault diagnosis, and system restoration. Since automation of systems has become increasingly complex and ubiquitous, troubleshooting skills are crucial to maintain safety and security in a variety of contexts. The planned study aims at examining troubleshooting strategies and how to induce them by means of simple visual aids and concise instructions. To this end, a computerized task consisting of network troubleshooting problems will be employed in an experimental study with repeated measures. Indicators of strategy use and performance will be tested for their relation to availability and differential use of visual aids, to cognitive styles that affect how individuals deal with challenges or system information, and to cognitive processes that are involved in metacognition and executive function. The planned research is expected to help gain insights into the cognitive determinants of troubleshooting, reverse engineering, and their links to computational thinking.

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