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Found 6 papers, 2 papers with code
Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers
With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains.
MLPerf Tiny Benchmark
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications.
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers
Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations.
Benchmarking TinyML Systems: Challenges and Direction
In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on car's brakes, it helps decreasing the probability of injuries and human fatalities.
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature.
