GeCo: Classification Restricted Boltzmann Machine Hardware for On-chip Learning
- Title
- GeCo: Classification Restricted Boltzmann Machine Hardware for On-chip Learning
- Authors
- YI, WOOSEOK; PARK, JUN KI; KIM, JAE JOON
- Date Issued
- 2017-10-19
- Publisher
- ACM/IEEE
- Abstract
- We present a Classification Restricted Boltzmann Machine (ClassRBM) hardware for embedded machines with on-chip learning capability. The RBM is a kind of the generative model, and has been used as one of the most popular feature extractors and image preprocessors. The ClassRBM is a variant of the RBM that is adapted to classification tasks. We propose the multi-Neuron-Per-Class (multi-NPC) voting scheme for improving accuracy of ClassRBM. We also show that the Contrastive Divergence (CD), which is one of the most popular algorithms to train RBM, has limitations in multi-NPC ClassRBM learning and propose a modified CD algorithm to overcome the limitation. Experimental results on FPGA flatform for MNIST datasets confirm that classification accuracy of the proposed algorithm is∼ 2.12% higher than the conventional CD.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/41876
- Article Type
- Conference
- Citation
- International Symposium on Rapid System Prototyping (RSP), 2017-10-19
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.