Titlebook: Embedded Computer Systems: Architectures, Modeling, and Simulation; 22nd International C Alex Orailoglu,Marc Reichenbach,Matthias Jung Conf

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TREAM: A Tool for?Evaluating Error Resilience of?,e-Based Models Using ,pproximate ,emorytion into the aforementioned parameters. Based on this, we construct a set of experiments using TREAM for different random forest structures and datasets over a set of bit error rates, where the relation between accuracy and bit error rate is considered for error resilience. The results demonstrate

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ControlPULP: A RISC-V Power Controller for?HPC Processors with?Parallel Control-Law Computation Accester system with a specialized DMA engine and a fast multi-core interrupt controller for parallel acceleration of real-time power management policies. ControlPULP relies on a real-time OS (FreeRTOS) to schedule a Power Control Firmware (PCF) software layer. We evaluate ControlPULP design choices in

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A Design Space Exploration Methodology for Enabling Tensor Train Decomposition in Edge Devicesstically pruned by removing inefficient solutions. Our experimental results prove that it is possible to output a limited set of solutions with better accuracy, memory, and FLOPs compared to the original (non-factorized) model. Our methodology has been developed as a standalone, parameterized module

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Study of?DNN-Based Ragweed Detection from?Dronesectivity. To overcome these challenges, we train state-of-the-art object detection and segmentation models on a ragweed dataset. The best performing segmentation models were compressed using shunt connections, fine-tuned with knowledge distillation, and further optimized with Nvidias TensorRT librar

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