Pearl: Performance-Aware Wear Leveling for Nonvolatile FPGAs
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2020.2998779
Abstract: Since static random access memory (SRAM)-based field-programmable gate array (FPGA) has limited density and comparatively high leakage power, researchers have proposed FPGA architectures based on emerging nonvolatile memories (NVMs) to satisfy the requirements of data-intensive… read more here.
Keywords: performance; wear leveling; pearl performance; leveling nonvolatile ... See more keywords
Exploiting Link Diversity for Performance-Aware and Repeatable Simulation in Low-Power Wireless Networks
Sign Up to like & getrecommendations! Published in 2020 at "IEEE/ACM Transactions on Networking"
DOI: 10.1109/tnet.2020.3016056
Abstract: Network simulation is a fundamental service for performance testing and protocol design in wireless networks. Due to the wireless dynamics, it is highly challenging to provide repeatable and reliable simulation results that are comparable to… read more here.
Keywords: protocol performance; performance; link diversity; wireless networks ... See more keywords
Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2020.2970013
Abstract: It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current centralized approaches to… read more here.
Keywords: oversubscription; resource; resource oversubscription; aware speculative ... See more keywords
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.48550/arxiv.2303.11923
Abstract: Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either single task model pruning or simply adapting… read more here.
Keywords: performance aware; global channel; task; channel pruning ... See more keywords