Work

Data-Efficient Conformalized Interval Prediction of Minimum Operating Voltage Capturing Process Variations

Year: 2024

Type: conference-paper

Abstract: Accurate minimum operating voltage (Vmin) prediction is a critical element in manufacturing tests. Conventional methods lack coverage guarantees in interval predictions. Conformal Prediction (CP), a distribution-free machine learning approach, excels in providing rigorous coverage guarantees for interval predictions. However, standard CP predictors may fail due to a lack of knowledge of process variations. We address this challenge by providing principled conformalized interval prediction in the presence of process variations with high data efficiency, where the data from a few additional chips is utilized for calibration. We demonstrate the superiority of the proposed method on industrial 16nm chip data.

Source: ACM/IEEE Design Automation Conference (DAC)

DOI: 10.1145/3649329.3657338

Authors: Yuxuan Yin, Rebecca Chen, Chen He, Peng Li

Institutions: University of California, Santa Barbara, NXP (United States)

Language: English

Topic: Low-power high-performance VLSI design

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: National Science Foundation