Mr. Gekko Budiutama Profile

Gekko Budiutama

at Tokyo Institute of Technology

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 19 December 2022 Presentation + Paper

Investigation of expressive power of a neural network architecture suited for optical neural networks

Gekko Budiutama, Sergei Manzhos, Manabu Ihara

Proceedings Volume 12318, 123180G (2022) https://doi.org/10.1117/12.2643705

KEYWORDS: Neural networks, Nonlinear optics, Neurons, Signal processing, Binary data, Machine learning, Computer simulations, Quantum dots, Photonics, Optical computing

Read Abstract +

In recent years, the concept of a fully optical neural network (ONN) has attracted attention for its potential to reach significantly higher speed and lower energy consumption compared to semiconductor electronics-based artificial neural network (ANN). Although the technology promises significant improvements through the utilization of light instead of digital signals, an ONN also requires physical fabrication on a planar substrate that introduces new limitations and constraints to the network when compared to the conventional NN. Limitations on node connectivity, choices of possible nonlinear neuron activation functions, as well as constraints on parameters in the case of a passive-circuit ONN are among the few peculiar challenges that are faced by the ONNs. In this study, we aim to understand the effect of ONNs’ limitations and constraints on their fitting ability when compared to the unconstrained NN. We use digital NNs that simulate the properties of ONNs to investigate the potential and limitations of the ONNs and might be useful as a practical way to pre-calibrate the ONNs’ parameters before their physical production. The ONNs performance and potential are evaluated and compared with the conventional NN by comparing their fitting ability on different datasets. Furthermore, the use-case for ONNs’ application is built and demonstrated on the material and financial datasets.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years