Name
Title | ||
|---|---|---|
Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks. |
Abstract | ||
|---|---|---|
In our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2505-7 | BMC Bioinformatics |
Keywords | Field | DocType |
Cancer,FPGA,Inference,Machine learning,Mass-spectrometry,Multi-layer perceptrons,Real-time | Real-time data,Biology,Latency (engineering),Interfacing,Field-programmable gate array,Bioinformatics,Artificial neural network,Perceptron,Embedded system,Communications protocol,Speedup | Journal |
Volume | Issue | ISSN |
19 | Suppl 18 | 1471-2105 |
Citations | PageRank | References |
3 | 0.39 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Sanaullah | 1 | 20 | 3.57 |
| Chen Yang | 2 | 57 | 20.71 |
| Yuri Alexeev | 3 | 9 | 1.90 |
| Kazutomo Yoshii | 4 | 249 | 18.53 |
| Martin C Herbordt | 5 | 507 | 57.27 |