SystemVerilog Random Sequence Generator

Tool

The SystemVerilog Random Sequence Generator is a language feature used in constrained-random verification to create randomized instruction sequences from structured rules and scenarios. Evidence from microprocessor verification use cases describes it as useful for improving stimulus quality, while also noting that procedural random-sequence generation does not fully exploit object-based constrained randomization.

First seen 5/28/2026

Last seen 6/5/2026

Evidence 5 chunks

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WIKI

Overview

The SystemVerilog Random Sequence Generator is described as a SystemVerilog language feature that can create randomized instruction sequences from a structured set of rules and scenarios. In the cited microprocessor-verification context, it is used to improve stimulus quality beyond simple random instruction streams.

Role in constrained-random verification

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RELATIONSHIPS

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Random Sequence Generation ← uses 97% 3e

Random sequence generation is supported by the SystemVerilog random sequence generator feature.

Constrained-Random Verification (CRV) ← uses 88% 1e

The SystemVerilog random sequence generator can create instruction sequences randomly but is procedural and does not fully exploit object-based randomization.

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Random Sequence Generation USES Extracted graph relationship

CITATIONS

7 sources

7 citations — click to expand

[1] The SystemVerilog Random Sequence Generator is a language feature that can create randomized instruction sequences from a structured set of rules and scenarios. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.

[2] In microprocessor verification, constrained-random verification addresses complexity from instruction sets, pipeline stages, execution strategies, instruction parallelism, scalar/vector operations, and many corner cases that make directed tests expensive to create. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.

[3] Simple random processor stimulus is insufficient because pure random instructions rarely target important functionality such as branches, jumps, and exceptions. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.

[4] A processor stimulus strategy may treat a program trace as a collection of instruction scenarios, such as boot code, exception handlers, configuration-register programming, load/store operations, arithmetic operations, branches, and nested branch loops. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.

[5] The cited source characterizes random-sequence generation schemes as procedural and says they do not fully exploit object-based randomization using constraints. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.

[6] The evidence describes an object-oriented processor-verification approach that models operations, instructions, and instruction scenarios as SystemVerilog classes. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.

[7] Effective constrained-random processor stimulus requires infrastructure with knowledge of the processor instruction-set architecture and state, and early planning for exception causes, probabilities, simultaneous exceptions, and priority handling. Constrained-random verification (CRV) offers a highly effective way to deal with the challenges of microprocessor verification.