input-stimuli fuzzing
ConceptInput-stimuli fuzzing is a hardware-verification approach adapted from software fuzzing techniques. It stresses a design-under-test externally—an “outside-in” approach—by generating or transforming inputs, in contrast to Logic Fuzzer’s “inside-out” fuzzing of RTL logic.
First seen 5/27/2026
Last seen 5/28/2026
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Overview
Input-stimuli fuzzing refers to hardware-verification techniques that adapt software-fuzzing ideas to hardware by stressing the design-under-test (DUT) externally. In the Logic Fuzzer paper’s taxonomy, these methods are explicitly characterized as an “outside-in” approach: they drive or generate stimuli for the DUT rather than fuzzing the DUT’s internal RTL logic.
Examples and related techniques
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3 connectionsLogic Fuzzer is distinguished from input-stimuli fuzzing, taking an inside-out approach rather than outside-in.
RFUZZ is an input-stimuli fuzzing tool adapted from American Fuzzy Lop for hardware verification.
PyMTL infrastructure adapted Hypothesis Testing for property-based input-stimuli testing.
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[1] Input-stimuli fuzzing adapts software-verification or software-fuzzing techniques for hardware verification and stresses the DUT externally as an outside-in approach. [PDF] Effective Processor Verification with Logic Fuzzer Enhanced Co ...
[2] RFUZZ is an example of input-stimuli fuzzing that transferred the concept of American Fuzzy Lop to hardware. [PDF] Effective Processor Verification with Logic Fuzzer Enhanced Co ...
[3] The PyMTL infrastructure adapted Hypothesis Testing, a property-based testing approach that constructs assertions and tries to find a minimal example that breaks an assertion. [PDF] Effective Processor Verification with Logic Fuzzer Enhanced Co ...
[4] Input-stimuli fuzzing is distinct from Logic Fuzzer: Logic Fuzzer fuzzes actual RTL logic, stirs execution paths while code runs, and is described as inside-out rather than outside-in. [PDF] Effective Processor Verification with Logic Fuzzer Enhanced Co ...