Split learning on FPGAs
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, September, 2020
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/129125 |
| _version_ | 1811070629031444480 |
|---|---|
| author | Whisnant, Hannah K. |
| author2 | Richard Younger and Frank R. Field, III. |
| author_facet | Richard Younger and Frank R. Field, III. Whisnant, Hannah K. |
| author_sort | Whisnant, Hannah K. |
| collection | MIT |
| description | Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, September, 2020 |
| first_indexed | 2024-09-23T08:39:04Z |
| format | Thesis |
| id | mit-1721.1/129125 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:39:04Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1291252022-01-31T17:32:02Z Split learning on FPGAs Split learning on field-programmable gate array Whisnant, Hannah K. Richard Younger and Frank R. Field, III. Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Technology and Policy Program. Massachusetts Institute of Technology. Institute for Data, Systems, and Society Technology and Policy Program Massachusetts Institute of Technology. Engineering Systems Division Institute for Data, Systems, and Society. Technology and Policy Program. Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, September, 2020 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 81-86). MIT Lincoln Laboratory is developing a software-reconfigurable imaging architecture called ReImagine, the first field programmable imaging array (FPIA), which reflects a broader trend in the increased use of FPGAs in sensor systems in order to reduce size and power consumption without a corresponding loss in performance or flexibility. At the same time, the field of machine learning is diversifying to include distributed deep learning methods like split learning, which can help preserve privacy by avoiding the sharing of raw data and model details. In order to continue to expand the capabilities of architectures like ReImagine's and enable split learning and related techniques to be used in the growing body of FPGA-based sensor systems, we examine the relationship of emerging split learning applications to FPGA-based image processing platforms. We determine that the implementation of split learning methods on FPGAs is feasible, and outline use cases in the areas of health and short timescale physics that demonstrate the usefulness of these implementations to both organizations concerned with privacy-preserving machine learning methods and organizations concerned with the deployment of efficient, flexible, and low-latency sensor systems. We begin by conducting a survey of the modern FPGA landscape in terms of technical attributes, use in sensor systems, security and privacy features, and current machine learning implementations. We also provide an overview of split learning and other distributed deep learning methods. Next, we synthesize an example split learning model in HDL code to demonstrate the feasibility of implementing such a model on an FPGA. Finally, we develop use cases for split learning applications on FPGA-based sensor systems and offer conclusions about the future development of distributed deep learning on heterogeneous processing platforms. by Hannah K. Whisnant. S.M. in Technology and Policy S.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program 2021-01-06T17:41:32Z 2021-01-06T17:41:32Z 2020 2020 Thesis https://hdl.handle.net/1721.1/129125 1227188838 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 86 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Institute for Data, Systems, and Society. Technology and Policy Program. Whisnant, Hannah K. Split learning on FPGAs |
| title | Split learning on FPGAs |
| title_full | Split learning on FPGAs |
| title_fullStr | Split learning on FPGAs |
| title_full_unstemmed | Split learning on FPGAs |
| title_short | Split learning on FPGAs |
| title_sort | split learning on fpgas |
| topic | Institute for Data, Systems, and Society. Technology and Policy Program. |
| url | https://hdl.handle.net/1721.1/129125 |
| work_keys_str_mv | AT whisnanthannahk splitlearningonfpgas AT whisnanthannahk splitlearningonfieldprogrammablegatearray |