Microcode Test Sequence

A microcode test sequence is a generated sequence of microprocessor instructions used in verification. In modern microprocessor verification, such sequences are often produced by automated random test generators rather than by hand-written directed tests, because processor complexity has increased and random generators can cover stimulus space more efficiently.^[1]

Purpose

Microcode test sequences are used to exercise instruction behavior across meaningful combinations of instruction attributes, including opcodes and related fields. The generation process emphasizes distributing stimulus across important values while allowing biasing toward corner cases.^[1]

Traditional sequential randomization of instruction fields can produce verbose and redundant code and may provide limited control over value distributions. A constrained-random approach can instead provide more precise distribution control and reduce generation overhead.^[1]

Constrained-Random Generation

SystemVerilog constraint-language constructs provide a concise way to describe microcode instructions in terms of valid attribute combinations. They also allow control over the distribution of individual field values.^[1]

An initial prototype used a single SystemVerilog class containing constraints for all opcodes. This approach improved on sequential randomization methods by enabling constraints across instruction attributes, but it also created a large constraint-solving problem.^[1]

Generator Architecture

The described opcode generator uses a two-layer architecture:^[1]

Upper layer — implemented with a SystemVerilog random sequence construct. It uses weighted knobs to control the distribution of high-level instruction items.^[1]
Lower layer — consists of opcode classes randomized with constraints and weights supplied by the upper layer.^[1]

Tests provide weighted values that direct the generator toward a required instruction mix. The constraint solver applies these weights at the generator layer to control the distribution of generated opcode types.^[1]

Single-Class Randomization

In the simplest style, all opcodes are represented in a single opcode class. This is flexible because constraints can be applied between any data members in the class.^[1]

However, this flexibility comes with a performance trade-off: the randomization problem can become slow because the constraint solver must process many random variables and a large set of constraints. In the cited implementation, the opcode class contained about 100 random variables and 800 constraint equations.^[1]

The single-class implementation included random variables and implication constraints to ensure only legal opcodes were generated. The opcode type was a key data member controlling which instruction type was produced.^[1]

Hierarchical Multi-Class Randomization

To reduce the size of the constraint-solving problem, the opcode class can be divided into multiple smaller classes. In the described object-oriented implementation, a base class held global constraints applying to all opcodes, while derived subclasses represented groups of related opcodes with similar constraints.^[1]

This hierarchical partitioning reduced memory requirements substantially and improved performance, while preserving control over instruction distribution and biasing.^[1]

Advantages

The hierarchical constrained-random approach provides several benefits for microcode test sequence generation:

Improved stimulus distribution across opcodes and instruction attributes.^[1]
Biasing support for targeting corner cases.^[1]
Reduced memory consumption by partitioning constraints into smaller opcode groups.^[1]
Better performance compared with solving a large monolithic opcode class.^[1]
Cleaner modeling of valid instruction combinations using SystemVerilog constraints.^[1]

References

^[1]: Gregory Tang and Rajat Bahl, AMD, Inc.; Alex Wakefield and Padmaraj Ramachandran, Synopsys Inc. Evidence excerpt on hierarchical constrained-random microcode test sequence generation using SystemVerilog and the Synopsys VCS constraint solver.