Titlebook: Embedded Computer Systems: Architectures, Modeling, and Simulation; 19th International C Dionisios N. Pnevmatikatos,Maxime Pelcat,Matthias

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Efficient Dynamic Device Placement for Deep Neural Network Training on Heterogeneous SystemsN model often requires very intensive computational resources. For edge incremental learning, more energy efficient learning solutions are called for. Heterogeneous computing is more power efficient, and has been increasingly popular for embedded platforms. Therefore, how to deploy training models o

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A Latency-Optimized Hash-Based Digital Signature Accelerator for the Tactile Internety and security. Security comprises encryption and data authentication. Digital signatures enable the latter. With the rise of quantum computers, most currently employed digital signature schemes will become unsecure. One promising post-quantum secure algorithm is the eXtended Merkle Signature Scheme

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Fault Sensitivity Analysis of Lattice-Based Post-Quantum Cryptographic ComponentsShor’s algorithm is expected in the near-to-medium future. Lattice-based PQC algorithms are among the most promising candidates discussed today, due to their performance and versatility. In this paper, we demonstrate fault sensitivity analysis (FSA) of circuit blocks used in lattice-based cryptograp

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Scalable Optimal Greedy Scheduler for Asymmetric Multi-/Many-Core Processorsng asymmetric many-core processors are expected to combine hundreds of cores belonging to different types. However, the accompanying task-to-core mapping schedules are the key to achieving the full potential of such processors. Run-time scheduling on asymmetric processors is a much harder problem to

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Platform-Agnostic Learning-Based Schedulingogeneous-ISA architectures with cores implementing different ISAs. As diversity of execution cores grows, predictive models become of paramount importance for scheduling and resource allocation. In this paper, we investigate the capabilities of performance predictors in a heterogeneous-ISA setting,