Neural network-based cooling design for high-performance processors.

ABSTRACT: Ultra-high chip power densities that are expected to surpass 1-2kW/cm² in future high-performance systems cannot be easily handled by conventional cooling methods. Various emerging cooling methods, such as liquid cooling via microchannels, thermoelectric coolers (TECs), two-phase vapor chambers, and hybrid cooling options have been designed to efficiently remove heat from high-performance processors. However, selecting the optimal cooling solution for a given chip and determining the optimal cooling parameters for that solution to achieve high efficiency are open problems. These problems are, in fact, computationally expensive because of the massive space of possible solutions. To address this design challenge, this article introduces a deep learning-based cooling design optimiz

SUBMITTER: Yuan Z 

PROVIDER: S-EPMC8717464 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature