Instruction Distillation
Instruction distillation is a technique for reducing the length of instruction sequences used as inputs in RTL-level hardware fuzzing while preserving their usefulness for bug detection and coverage exploration. In the context of the RTL fuzzer INSTILLER—short for Instruction Distiller—instruction distillation is used to keep fuzzing inputs short because prior CPU fuzzing approaches can generate increasingly long RTL instruction inputs, and longer inputs may be ineffective for fuzzing.[1]
Background
Hardware bugs, including CPU bugs, differ from software bugs in that they generally need to be detected before deployment.[1] RTL fuzzing targets hardware designs at the register-transfer level and attempts to generate inputs that expose mismatches, bugs, or unusual execution behavior before hardware is manufactured or released.
Earlier CPU fuzzing work faced several limitations. One identified problem is that generated RTL input instruction sequences tend to grow in length over time, which reduces fuzzing efficiency because longer inputs are not necessarily more effective.[1] Instruction distillation addresses this by reducing instruction-sequence length during the fuzzing process.
INSTILLER
INSTILLER is an RTL fuzzer based on ant colony optimization (ACO).[1] Its core contribution is the use of a variant called VACO to distill input instructions so that fuzzing inputs remain short and efficient.[1]
In addition to instruction distillation, INSTILLER addresses two other fuzzing concerns:
- Realistic interruption and exception modeling — prior work did not simulate realistic interruptions well, so INSTILLER includes mechanisms for inserting interruptions and exceptions when generating fuzzing inputs.[1]
- Hardware-based fuzzing improvements — INSTILLER uses hardware-based seed selection and mutation strategies to improve fuzzing performance.[1]
Instruction Distillation Method
Within INSTILLER, instruction distillation is performed using VACO, a variant of ant colony optimization.[1] The purpose of the algorithm is to reduce the size of RTL instruction inputs while retaining fuzzing effectiveness. By keeping instruction sequences short, INSTILLER aims to improve execution speed and avoid wasting fuzzing effort on excessively long inputs.[1]
The evidence describes VACO as a mechanism that “distills input instructions,” but does not provide implementation-level details such as pheromone update rules, cost functions, or selection probabilities.[1]
Evaluation Results
INSTILLER was evaluated against state-of-the-art RTL fuzzing work on real-world target CPU cores.[1] Reported results include:
| Metric | Reported Result |
|---|---|
| Coverage improvement over DiFuzzRTL | 29.4% more coverage |
| Mismatch detection improvement | 17.0% more mismatches |
| Input length reduction using VACO | 79.3% shorter input instructions than DiFuzzRTL |
| Execution speed improvement from distillation | 6.7% average increase |
These results suggest that instruction distillation can reduce RTL fuzzing input length substantially while also improving coverage, mismatch detection, and execution speed in the evaluated setting.[1]
Significance
Instruction distillation is significant because it targets a practical bottleneck in CPU RTL fuzzing: the growth of input instruction sequences. By shortening inputs through VACO, INSTILLER improves fuzzing efficiency while maintaining or increasing effectiveness in terms of coverage and mismatch detection.[1]
References
[1]: “INSTILLER: Towards Efficient and Realistic RTL Fuzzing,” arXiv:2401.15967, DOI: 10.48550/arXiv.2401.15967.