PPAC Driven Multi-die and Multi-technology Floorplanning
In heterogeneous integration, where different dies may utilize distinct technologies, floorplanning across multiple dies inherently requires simultaneous technology selection. This work presents the first systematic study of multi-die and multi-technology floorplanning. Unlike many conventional approaches, which are primarily driven by area and wirelength, this study additionally considers performance, power, and cost, highlighting the impact of technology selection. A simulated annealing method and a reinforcement learning techniques are developed. Experimental results show that the proposed techniques significantly outperform a naive baseline approach.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning
Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplanning stage especially 3D scenarios with multiple stacked layers, it has become in…
Reinforcement LearningRapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine
This paper introduces the Pandemic PACT Advanced Categorisation Engine (PPACE) along with its associated dataset. PPACE is a fine-tuned model developed to automatically classify research abstracts from funded biomedical …
Decision MakingDocument ClassificationLanguage ModellingLarge Language ModelDPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning models from data. We develop the open-sou…
DescriptivePrivacy PreservingregressionFloorplanning of VLSI by Mixed-Variable Optimization
By formulating the floorplanning of VLSI as a mixed-variable optimization problem, this paper proposes to solve it by memetic algorithms, where the discrete orientation variables are addressed by the distribution evoluti…
EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing
Relation classification (RC) aims to predict the relationship between a pair of subject and object in a given context. Recently, prompt tuning approaches have achieved high performance in RC. However, existing prompt tun…
ClassificationRelationRelation Classification