Weighted Distribution
ConceptWeighted distribution is a constrained-random test-generation technique in which weighted values or knobs bias the mix of generated stimulus, such as opcode types or instruction-field values, so verification can control coverage emphasis and target corner cases.
First seen 5/28/2026
Last seen 5/28/2026
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Overview
Weighted distribution is used in constrained-random verification to control how often different stimulus choices are generated. In the cited microcode-stimulus generator, tests provide a set of weighted values that direct the generator toward the required mix of instructions, and the constraint solver applies those weights to control the distribution of generated opcode types.
Role in constrained-random generation
NEIGHBORHOOD
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2 connectionsThe generator uses weighted distributions to control opcode type distribution.
The opcode generator applies weights to control distribution of opcode types.
CITATIONS
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[1] Weighted values direct the generator to the required mix of instructions and are applied by the constraint solver to control opcode-type distribution. Generating AMD microcode stimuli using VCS constraint solver
[2] SystemVerilog constraint-language constructs provide precise control over the distribution of values for individual instruction fields. Generating AMD microcode stimuli using VCS constraint solver
[3] The opcode-generator architecture uses an upper random-sequence layer with weighted knobs and a lower randomized opcode-class layer that receives additional constraints and weights. Generating AMD microcode stimuli using VCS constraint solver
[4] Weighted distribution and biasing are used in the hierarchical constrained-random approach to help hit corner cases. Generating AMD microcode stimuli using VCS constraint solver