Name
Abstract | ||
|---|---|---|
Deep Neural Networks (DNNs) have a wide application scope beyond computer vision tasks, promising to replace manual algorithmic implementations in applications ranging from large-scale physics experiments to next-generation network security. Such applications may require data processing rates in the millions of samples per second and sub-microsecond latency, which is possible with customized FPGA or ASIC implementations. We present a novel method called LogicNets for co-design of DNN topologies and hardware circuits that maps to a very efficient FPGA implementation to address the needs of such applications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/FCCM48280.2020.00071 | 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
Keywords | DocType | ISSN |
high-throughput DNN inference,LogicNets,deep neural networks,computer vision,large-scale physics experiments,next-generation network security,data processing rates,DNN topologies,FPGA,submicrosecond latency,hardware circuits | Conference | 2576-2613 |
ISBN | Citations | PageRank |
978-1-7281-5804-4 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaman Umuroglu | 1 | 186 | 10.67 |
| Yash Akhauri | 2 | 1 | 0.72 |
| Nicholas J. Fraser | 3 | 177 | 12.85 |
| Michaela Blott | 4 | 315 | 25.60 |