Adopting Decision Tree Based Policy Enforcement Mechanism to Protect Reconfigurable Devices

Barbareschi, Mario; Mazzeo, Antonino; Miranda, Salvatore

doi:10.1007/978-3-319-39345-2_7

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Authors of [7,8,10] proposed a novel approach for an efficient hardware implementation of MCSs: parallel classification entities-for instance, DTs-execute classification in parallel, and then, an hardware combiner organizes outputs from different DT classifiers in order to make the final decision. DT nodes work in parallel and each one is implemented as a binary comparator: once it receives the feature value, it returns a Boolean value that, in turn, is fed to a Boolean network in order to compute which leaf of the tree has been reached.…”

Section: Decision Tree-based Hardware Classifiersmentioning

confidence: 99%

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Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study

2021

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So far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit approximate computing design paradigm to trade hardware area overhead off for classification accuracy. In particular, starting from trained DT models and employing precision-scaling technique, we explore approximate decision tree variants by means of multiple objective optimization problem, demonstrating a significant performance improvement targeting field-programmable gate array devices.

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Section: Decision Tree-based Hardware Classifiersmentioning

confidence: 99%

“…For instance, this is the case of hardware acceleration of decision trees (DTs) classification systems based on the field-programmable gate array devices (FPGAs) [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%