Automatic Cross-Layer Synthesis of High Performance, (Ultra-)Low Power Hardware Implementations from Data Flow Specifications by Integration of Emerging FeFET Technology (HiLoDa Nets)

Third party funded individual grant


Acronym: HiLoDa Nets

Start date : 01.03.2024

End date : 01.03.2027

Website: https://www.cs12.tf.fau.de/forschung/projekte/hiloda-nets/


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Types of publications

Journal article
Book chapter / Article in edited volumes
Authored book
Translation
Thesis
Edited Volume
Conference contribution
Other publication type
Unpublished / Preprint

Publication year

From
To

Abstract

Journal

FeMFET-based High-Performance, Ultra-Low Power Memory Cells for Reliable State Retention of Dataflow Networks (2026) Karim A, Darne B, Matrangolo PA, Falk J, O'Connor I, Marchand C, Bosio A, Teich J Journal article Exploration of Clock and Power Gating Tradeoffs for the Design of Self-Powering Dataflow Networks (2025) Karim A, Falk J, Teich J Conference contribution, Conference Contribution Non-Volatile Ferroelectric-AND (FeAND) Memory Cell Design (2025) Darne B, Filsinger M, Bosio A, Deleruyelle D, O'Connor I, Vilquin B, Marchand C Conference contribution, Conference Contribution FeMFET-based High Performance, Ultra-Low Power Memory Cells for Reliable State Retention of Dataflow Networks (2025) Darne B, Karim A, Matrangolo PA, Falk J, O'Connor I, Marchand C, Bosio A, Teich J Conference contribution, Conference Contribution Self-Powering Dataflow Networks – Concepts and Implementation (2024) Karim A, Falk J, Schmidt D, Teich J Conference contribution, Conference Contribution Exploring Multi-Reader Buffers and Channel Placement during Dataflow Network Mapping to Heterogeneous Many-core Systems (2024) Letras M, Falk J, Teich J Journal article, Online publication Techniques for Efficient Performance Analysis and Memory Optimization in Mapping Dataflow Models of Computation onto Embedded Systems (2024) Letras M Thesis Special Session - Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications (2023) Henkel J, Sidduh L, Bauer L, Teich J, Wildermann S, Tahoori MB, Mayahinia M, et al. Conference contribution, Original article