Name
Abstract | ||
|---|---|---|
The goal of this work is the design and implementation of a low-cost system-on-FPGA for handwritten digit recognition, based on a relatively deep and wide network of perceptrons. In order to increase the performance of the application on embedded processors whose performances are way below standard general purpose CPUs, a regularization method was used during the training phase of the neural network that allows for the drastic reduction of floating point operations. Our implementation can achieve a 3× speed-up toward a raw implementation without optimization, while keeping the accuracy in acceptable ranges. Our efforts reinforce the fact that FPGAs are suited for deploying complex artificial intelligence modules. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/FCCM.2015.44 | Field-Programmable Custom Computing Machines |
Field | DocType | Citations |
Computer science,Floating point,Parallel computing,Field-programmable gate array,Image processing,Chip,Time delay neural network,Regularization (mathematics),Artificial neural network,Perceptron | Conference | 1 |
PageRank | References | Authors |
0.37 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Bochi Saldanha | 1 | 7 | 1.79 |
| Christophe Bobda | 2 | 627 | 90.57 |