Search NASA⌕ Search

DOE OSTI · 1632554

Setting operating points for circuits in an integrated circuit chip using an integrated voltage regulator power loss model

Abstract

An apparatus includes an integrated circuit chip with a set of circuits having two or more subsets of circuits; an external voltage regulator separate from the integrated circuit chip; two or more integrated voltage regulators on the integrated circuit chip that each provide an input voltage to a respective subset of the circuits; and a controller. The controller determines, using an integrated voltage regulator power loss model, an electrical power loss for the integrated voltage regulators for a first combination of operating points for the subsets of the circuits. The controller then determines, based on the electrical power loss, a second combination of operating points for the subsets of the circuits that includes an adjustment to an operating point for at least one of the subsets of the circuits that compensates for an electrical power loss of the corresponding integrated voltage regulator. The controller sets an operating point of each of the subsets of the circuits based on the second combination of operating points.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Huang, Wei, Rodriguez, Miguel, Rao, Karthik. 2020-02-11. Setting operating points for circuits in an integrated circuit chip using an integrated voltage regulator power loss model. https://www.osti.gov/biblio/1632554

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗