Embedded Computer Systems: Architectures, Modeling, and Simulation
Springer International Publishing (Verlag)
978-3-031-15073-9 (ISBN)
The 21 full papers presented in this volume were carefully reviewed and selected from 44 submissions. The papers are organized in topics as follows: High level synthesis; memory systems; processor architecture; embedded software systems and beyond; deep learning optimization; extra-functional property estimation; innovative architectures and tools for security; european research projects on digital systems, services, and platforms.
High Level Synthesis.- High-Level Synthesis of Digital Circuits from Template Haskell and SDF-AP 1 H. H. .- Implementing Synthetic Aperture Radar Backprojection in Chisel - A Field Report.-EasyHBM: Simple and Fast HBM Access for FPGAs using High-Level Synthesis.- Memory Systems.- TREAM: A Tool for Evaluating Error Resilience of Tree-based Models using Approximate Memory.-Split'n'Cover: ISO 26262 Hardware Safety Analysis with SystemC.- Tagged Geometric History Length Access Interval Prediction for Tightly Coupled Memory Systems.- Processor Architecture.-NanoController: A Minimal and Flexible Processor Architecture for UltraLow-Power.- ControlPULP: A RISC-V Power Controller for HPC Processors with Parallel Control-Law Computation Acceleration.- Embedded Software Systems and beyond.-CASA: An Approach for exposing and documenting Concurrency-related Software Properties.- High-Level Simulation of Embedded Software Vulnerabilities to EM SideChannel Attacks.- Deep Learning Optimization I.-A Design Space Exploration Methodology for Enabling Tensor Train Decomposition in Edge Devices.- Study of DNN-based Ragweed Detection from Drones.- PULP-TrainLib: Enabling On-Device Training for RISC-V Multi-Core MCUs through Performance-Driven Autotuning.-Extra-functional Property Estimation.- The Impact of Dynamic Storage Allocation on CPython Execution Time, Memory Footprint and Energy Consumption: An Empirical Study.- Application runtime estimation for AURIX embedded MCU using deep learning.-A Hybrid Performance Prediction Approach for Fully-Connected Artificial Neural Networks on Multi-Core Platforms.- Deep Learning Optimization I.- A Smart HW-Accelerator for Non-Uniform Linear Interpolation of MLActivation Functions.-Hardware-Aware Evolutionary Filter Pruning.- Innovative Architectures and tools for Security.- Obfuscating the Hierarchy of a Digital IP.-On the effectiveness of true random number generators implemented on FPGAs.- Power and Energy.- SIDAM: A Design Space Exploration Framework for Multi-Sensor Embedded Systems Powered by Energy Harvesting.- A Data-Driven Approach to Lightweight DVFS-Aware Counter-Based Power Modeling for Heterogeneous Platforms.
| Erscheinungsdatum | 16.08.2022 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | XIV, 436 p. 31 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 679 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Informatik ► Weitere Themen ► Hardware | |
| Schlagworte | Applications • Artificial Intelligence • Computer Hardware • Computer Networks • computer programming • Computer Science • Computer systems • conference proceedings • distributed computer systems • Distributed Systems • Embedded Systems • Engineering • Field Programmable Gate Array (FPGA) • Informatics • Internet • machine learning • Mathematics • Microprocessor chips • Network Protocols • parallel processing systems • Processors • Research • Signal Processing • Telecommunication Systems |
| ISBN-10 | 3-031-15073-2 / 3031150732 |
| ISBN-13 | 978-3-031-15073-9 / 9783031150739 |
| Zustand | Neuware |
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