NASA · NASA-855 · record R0459

AI: Logic Design and Verification

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

There has been much discussion on the use of Artificial Intelligence (AI) in many fields. This study is a first look at the use of AI in the field of digital electronics, primarily generating VHDL designs as well as self-checking test benches. A series of experiments were conducted, giving the AI design and verification tasks for standard logic constructions as well as modest-sized applications. The AI’s performance was analyzed in detail.

Expected benefits

The key benefit of using AI for these applications is the huge speed advantage for generating designs and test benches, when compared to the human. This is for NASA and organizations that do similar engineering; not for the general public. In the course of this study, some cases that would take a human a number of hours to complete, would be done in less than a minute by the AI.

System outputs

The error or anomaly rate was relatively high for the AI. Indeed, it would be higher than a young engineer. Some of the anomalies were minor and could be corrected by the human or be directing the AI. Other anomalies were moderate to major; a considerable amount of human time would be necessary to fix the problems; in some cases the AI output would have to be replaced by the human.

Technology

AI classification: Reinforcement learningSource: Reinforcement Learning: AI trained through trial and error using rewards and penalties to optimize decision-making policies.
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported