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ARM-CO-UP: ARM COoperative Utilization of Processors
The paper 'Analyzing Per-Application Energy Consumption in a Multi-Application Computing Continuum' was accepted at IEEE FMEC '24.
Analyzing Per-Application Energy Consumption in a Multi-Application Computing Continuum
PiQi: Partially Quantized DNN Inference on HMPSoCs
The paper 'Lifetime Estimation for Core-Failure Resilient Multi-Core Processors' was accepted for IEEE MCSoC '23.
The paper '3D-TTP: Efficient Transient Temperature-Aware Power Budgeting for 3D-Stacked Processor-Memory Systems' was accepted for publication at IEEE ISVLSI '23.
The patent 'Power management for multicore processors' has been successfully published under number EP 4 152 124.
Edge AI
Thermals
The chapter 'Pipelined CNN Inference on Heterogeneous Multi-Processor System-on-Chip' was accepted for the book on Embedded Machine Learning for Cyber Physical, IoT, and Edge Computing (Springer).
The paper 'CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5 D, and 3D Processor-Memory System' was accepted for ACM Transactions on Architecture and Code (TACO).
HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction
T-TSP
The paper 'HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction' was accepted for IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
CoMeT
HotSniper
EnergyLabels
HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction
The paper 'ChordMap: Automated Mapping of Streaming Applications onto CGRA' was accepted for IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
ChordMap: Automated Mapping of Streaming Applications onto CGRA
CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory Systems
Neural Network-Based Performance Prediction for Task Migration on S-NUCA Many-Cores
Power-Efficient Heterogeneous Many-Core Design With NCFET Technology
The paper 'HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction' was accepted for DATE 2021.
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