Input Instruction Distillation
TechniqueInput Instruction Distillation is a technique proposed for Instiller, an RTL fuzzing prototype. It shortens input instruction sequences while preserving original coverage, using a variant of ant colony optimization, and was reported to improve execution speed by 6.7% on average.
First seen 5/27/2026
Last seen 6/3/2026
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Overview
Input Instruction Distillation is a technique proposed in the Instiller RTL fuzzing work to reduce CPU cycles and improve fuzzing performance by shortening input instruction length. Its basic idea is to construct a subset of the original input set that is shorter while maintaining the original coverage.
Method
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7 connectionsInput instruction distillation uses the variant of ant colony optimization (VACO) as its core algorithm.
Instiller implements input instruction distillation to shorten and improve the effectiveness of input instructions.
Instiller uses input instruction distillation to keep input instruction length short and efficient.
Input instruction distillation operates on instruction sequences to shorten them while maintaining coverage.
Instiller introduces input instruction distillation as a novel contribution.
Input instruction distillation is derived from the ant colony optimization technique.
Input instruction distillation constructs shorter inputs that maintain original coverage.
CITATIONS
9 sources9 citations — click to expand
[1] Input Instruction Distillation is proposed as a technique in the Instiller paper. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[2] The basic idea of Input Instruction Distillation is to construct a shorter subset of the original input set while maintaining original coverage. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[3] Input Instruction Distillation is based on a variant of ant colony optimization. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[4] The Instiller paper models input instruction length as the number of ants and RTL circuits as cities, and the algorithm outputs the best input instruction and length for the current status. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[5] The authors modified classic ant colony optimization and proposed VACO to fit the RTL fuzzing scenario. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[6] Input Instruction Distillation can make inputs shorter and more effective. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[7] For input instruction distillation, Instiller's length is reported as 79.3% shorter than DiFuzzRTL. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[8] Input instruction distillation leads to a 6.7% increase in execution speed on average. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing
[9] Instiller finds 17.0% more mismatches in the targets. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing