In this paper, we report a study on hardware implementation of a Deterministic Boltzmann Machine (DBM) with non-monotonic neurons (non-monotonic DBM network). The hardware DBM network has fewer components than other neural networks. Results from numerical simulations show that the non-monotonic DBM network has high learning ability as compared to the monotonic DBM network. These results show that the non-monotonic DBM network has large potential for the implementation of a high functional neurochip. Then, we design and fabricate a neurochip of the non-monotonic DBM network of which measurement confirms that the high-functional large-scale neural system can be realized on a compact neurochip by using the non-monotonic neurons.
|Number of pages||10|
|Journal||IEICE Transactions on Information and Systems|
|Publication status||Published - 2002 Mar|
- Analog circuit
- DBM learning
- Neural network