Name
Title | ||
|---|---|---|
FA-LAMP: FPGA-Accelerated Learned Approximate Matrix Profile for Time Series Similarity Prediction |
Abstract | ||
|---|---|---|
With the proliferation of low-cost sensors and the Internet-of-Things (IoT), the rate of producing data far exceeds the compute and storage capabilities of today’s infrastructure. Much of this data takes the form of time series, and in response, there has been increasing interest in the creation of time series archives in the last decade, along with the development and deployment of novel analysis... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/FCCM51124.2021.00013 | 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
Keywords | DocType | ISSN |
Training,Deep learning,Correlation,Time series analysis,Neural networks,Prediction algorithms,Real-time systems | Conference | 2576-2613 |
ISBN | Citations | PageRank |
978-1-6654-3555-0 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amin Kalantar | 1 | 0 | 0.34 |
| Zachary Zimmerman | 2 | 45 | 5.04 |
| Philip Brisk | 3 | 80 | 10.05 |