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Found 3 papers, 1 papers with code
QuTiBench: Benchmarking Neural Networks on Heterogeneous Hardware
In order to address these implementation challenges, a broad spectrum of new customized and heterogeneous hardware architectures have emerged, often accompanied with co-designed algorithms to extract maximum benefit out of the hardware.
Hardware Architecture
Spectrum Monitoring for Radar Bands using Deep Convolutional Neural Networks
The core of our framework is a deep convolutional neural network (CNN) model that enables Measurement Capable Devices to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial LTE and WLAN.
Deep Activity Recognition Models with Triaxial Accelerometers
Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data.
