Carlos Valderrama

TL;DR: A performance analysis of the FPGA and the GPU implementations, and an extra CPU reference implementation, shows the competitive throughput of the proposed architecture even at a much lower clock frequency than those of the GPU and the CPU.

TL;DR: This approach addresses the modeling of communication between the hardware and software modules at different abstraction levels and for different design tools by using a multi-view library concept in order to hide specific hardware/software implementation details and communication schemes.

TL;DR: In this paper, the authors evaluated the performance of ECG, thermistor, chest belt, accelerometer, contact, and audio microphones for the detection of chronic chronic cough disease using three stages: mutual information conveyed by the features, ability to discriminate at the frame level cough from these latter other sources of ambiguity, and ability to detect cough events.

TL;DR: The proposed approach is shown to clearly outperform the commercial Karmelsonix system which achieved a specificity of 95.3% and a sensitivity of 64.9% in this latter experiment.

TL;DR: This paper briefly review recent work related to the implementation of deep learning algorithms in FPGAs, and analyzes and compares the design requirements and features of existing topologies to finally propose development strategies and implementation architectures for better use of FPGA-based deep learning topologies.