Abstract
Due to their flexible architecture and inherent parallelism, FPGAs are ideal candidates for neural network implementations. Still they have not achieved wide-spread acceptance in this regard. One of the major roadblocks for FPGAs is the implementation of complex mathematical functions encountered in neural networks. Exact implementation of these functions consume large number of resources. In this paper we discuss an FPGA-based neural network prototyping platform and the approximate implementation of a probabilistic neural network (PNN) on a Xilinx 7-Series FPGA. The complex mathematical functions as replaced by approximations. Analysis shows that hardware performance is much higher than that of software counter parts and the error induced due to approximations is within tolerable limit.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 64-68 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538659281 |
| DOIs | |
| Publication status | Published - Sep 28 2018 |
| Event | 2nd International Conference on Computing and Network Communications, CoCoNet 2018 - Astana, Kazakhstan Duration: Aug 15 2018 → Aug 17 2018 |
Conference
| Conference | 2nd International Conference on Computing and Network Communications, CoCoNet 2018 |
|---|---|
| Country | Kazakhstan |
| City | Astana |
| Period | 8/15/18 → 8/17/18 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Electrical and Electronic Engineering
Cite this
FAPNN : An FPGA based Approximate Probabilistic Neural Network Library. / Vipin, Kizheppatt; Akhmetov, Yerbol; Myrzakhme, Serikbolsyn; James, Alex P.
Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 64-68 8476889.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - FAPNN
T2 - An FPGA based Approximate Probabilistic Neural Network Library
AU - Vipin, Kizheppatt
AU - Akhmetov, Yerbol
AU - Myrzakhme, Serikbolsyn
AU - James, Alex P.
PY - 2018/9/28
Y1 - 2018/9/28
N2 - Due to their flexible architecture and inherent parallelism, FPGAs are ideal candidates for neural network implementations. Still they have not achieved wide-spread acceptance in this regard. One of the major roadblocks for FPGAs is the implementation of complex mathematical functions encountered in neural networks. Exact implementation of these functions consume large number of resources. In this paper we discuss an FPGA-based neural network prototyping platform and the approximate implementation of a probabilistic neural network (PNN) on a Xilinx 7-Series FPGA. The complex mathematical functions as replaced by approximations. Analysis shows that hardware performance is much higher than that of software counter parts and the error induced due to approximations is within tolerable limit.
AB - Due to their flexible architecture and inherent parallelism, FPGAs are ideal candidates for neural network implementations. Still they have not achieved wide-spread acceptance in this regard. One of the major roadblocks for FPGAs is the implementation of complex mathematical functions encountered in neural networks. Exact implementation of these functions consume large number of resources. In this paper we discuss an FPGA-based neural network prototyping platform and the approximate implementation of a probabilistic neural network (PNN) on a Xilinx 7-Series FPGA. The complex mathematical functions as replaced by approximations. Analysis shows that hardware performance is much higher than that of software counter parts and the error induced due to approximations is within tolerable limit.
UR - http://www.scopus.com/inward/record.url?scp=85055980488&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055980488&partnerID=8YFLogxK
U2 - 10.1109/CoCoNet.2018.8476889
DO - 10.1109/CoCoNet.2018.8476889
M3 - Conference contribution
AN - SCOPUS:85055980488
SP - 64
EP - 68
BT - Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -