Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures
High-performance communications links and the increasing reliance on artificial intelligence are creating an exponentially increasing demand for higher data rates and computing performance. However, Moore’s Law of exponentially growing computing capabilities has slowed, meaning that the traditional...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156317 |
| _version_ | 1826204629916975104 |
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| author | Davis III, Ronald A. |
| author2 | Englund, Dirk |
| author_facet | Englund, Dirk Davis III, Ronald A. |
| author_sort | Davis III, Ronald A. |
| collection | MIT |
| description | High-performance communications links and the increasing reliance on artificial intelligence are creating an exponentially increasing demand for higher data rates and computing performance. However, Moore’s Law of exponentially growing computing capabilities has slowed, meaning that the traditional computing architecture has reached a bottleneck in processing performance, largely due to data movement. Considerable efforts have been made to create custom hardware to accelerate deep neural network training and inference. Among these efforts are optical neural networks (ONNs), which have been a promising approach that excel at linear operations but struggle with nonlinear implementations and scalability. Here, an LTI Simulation Toolkit has been created to facilitate rapid iterative photonic circuit designing to quickly evaluate ONN architectures. The LTI Toolkit interprets the electromagnetic (EM) waves as LTI inputs into a transfer function that obeys the analytical solutions of the photonic components. Thus, this LTI toolkit was a stepping stone to designing the primary result of this work—the ONN architecture called the multiplicative analog frequency transform optical neural network (MAFT-ONN), implementing single-shot matrix products and single-shot nonlinear activations using a single device for all neurons in a layer. We demonstrate its RF signal processing capabilities by experimentally implementing various signal processing operations like matched filters, Wiener filters, and linear signal estimation as well as a 3-layer DNN for modulation classification of raw RF signals, thus being the first recorded analog hardware accelerator to ever perform deep learning directly on raw RF signals. We also present a system-level analysis to quantitatively show that the MAFT architecture is hundreds of times faster than even the theoretical peak performance of modern digital architectures in communications systems. |
| first_indexed | 2024-09-23T12:58:29Z |
| format | Thesis |
| id | mit-1721.1/156317 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:58:29Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1563172024-08-22T03:32:06Z Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures Davis III, Ronald A. Englund, Dirk Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science High-performance communications links and the increasing reliance on artificial intelligence are creating an exponentially increasing demand for higher data rates and computing performance. However, Moore’s Law of exponentially growing computing capabilities has slowed, meaning that the traditional computing architecture has reached a bottleneck in processing performance, largely due to data movement. Considerable efforts have been made to create custom hardware to accelerate deep neural network training and inference. Among these efforts are optical neural networks (ONNs), which have been a promising approach that excel at linear operations but struggle with nonlinear implementations and scalability. Here, an LTI Simulation Toolkit has been created to facilitate rapid iterative photonic circuit designing to quickly evaluate ONN architectures. The LTI Toolkit interprets the electromagnetic (EM) waves as LTI inputs into a transfer function that obeys the analytical solutions of the photonic components. Thus, this LTI toolkit was a stepping stone to designing the primary result of this work—the ONN architecture called the multiplicative analog frequency transform optical neural network (MAFT-ONN), implementing single-shot matrix products and single-shot nonlinear activations using a single device for all neurons in a layer. We demonstrate its RF signal processing capabilities by experimentally implementing various signal processing operations like matched filters, Wiener filters, and linear signal estimation as well as a 3-layer DNN for modulation classification of raw RF signals, thus being the first recorded analog hardware accelerator to ever perform deep learning directly on raw RF signals. We also present a system-level analysis to quantitatively show that the MAFT architecture is hundreds of times faster than even the theoretical peak performance of modern digital architectures in communications systems. Ph.D. 2024-08-21T18:56:20Z 2024-08-21T18:56:20Z 2024-05 2024-07-10T13:01:29.788Z Thesis https://hdl.handle.net/1721.1/156317 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Davis III, Ronald A. Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures |
| title | Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures |
| title_full | Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures |
| title_fullStr | Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures |
| title_full_unstemmed | Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures |
| title_short | Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures |
| title_sort | combining rf machine learning and rf photonics to enable new analog communications architectures |
| url | https://hdl.handle.net/1721.1/156317 |
| work_keys_str_mv | AT davisiiironalda combiningrfmachinelearningandrfphotonicstoenablenewanalogcommunicationsarchitectures |