Abstract
Approximating kernel functions with random features (RFs) has been a successful application of random projections for nonparametric estimation. However, performing random projections presents computational challenges for large-scale problems. Recently, a new optical hardware called Optical Processing Unit (OPU) has been developed for fast and energy-efficient computation of large-scale RFs in the analog domain. More specifically, the OPU performs the multiplication of input vectors by a large random matrix with complexvalued i.i.d. Gaussian entries, followed by the application of an element-wise squared absolute value operation-this last nonlinearity being intrinsic to the sensing process. In this paper, we show that this operation results in a dot-product kernel that has connections to the polynomial kernel, and we extend this computation to arbitrary powers of the feature map. Experiments demonstrate that the OPU kernel and its RF approximation achieve competitive performance in applications using kernel ridge regression and transfer learning for image classification. Crucially, thanks to the use of the OPU, these results are obtained with time and energy savings.
Original language | English (US) |
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Title of host publication | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 9294-9298 |
Number of pages | 5 |
ISBN (Electronic) | 9781509066315 |
DOIs | |
State | Published - May 2020 |
Event | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: May 4 2020 → May 8 2020 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2020-May |
ISSN (Print) | 1520-6149 |
Conference
Conference | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 |
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Country/Territory | Spain |
City | Barcelona |
Period | 05/4/20 → 05/8/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Kernel methods
- kernel ridge regression
- nonparametric estimation
- optical computing
- random features
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering