Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/26404
Author(s): | Miranda, I. D. S. Arora, A. Susskind, Z. Villon, L. A. Q. Katopodis, R. F. Dutra, D. L. C. Araújo, L. S. de. Lima, P. M. V. França, F. M. G. John, L. K. Breternitz Jr., M. |
Date: | 2022 |
Title: | LogicWiSARD: Memoryless synthesis of weightless neural networks |
Book title/volume: | 2022 IEEE 33rd International Conference on Application-specific Systems, Architectures and Processors (ASAP) |
Pages: | 19 - 26 |
Event title: | 33rd International Conference on Application-specific Systems, Architectures and Processors (ASAP) |
Reference: | Miranda, I. D. S., Arora, A., Susskind, Z., Villon, L. A. Q., Katopodis, R. F., Dutra, D. L. C., Araújo, L. S. de., Lima, P. M. V., França, F. M. G., John, L. K., & Breternitz Jr., M. (2022). LogicWiSARD: Memoryless synthesis of weightless neural networks. In 33rd International Conference on Application-specific Systems, Architectures and Processors (pp. 19-26). IEEE. https://doi.org/10.1109/ASAP54787.2022.00014 |
ISSN: | 2160-0511 |
ISBN: | 978-1-6654-8308-7 |
DOI (Digital Object Identifier): | 10.1109/ASAP54787.2022.00014 |
Keywords: | Weightless neural networks WiSARD FPGA VLSI |
Abstract: | Weightless neural networks (WNNs) are an alternative pattern recognition technique where RAM nodes function as neurons. As both training and inference require mostly table lookups, few additions, and no multiplications, WNNs are suitable for high-performance and low-power embedded applications. This work introduces a novel approach to implement WiSARD, the leading WNN state-of-the-art architecture, completely eliminating memories and arithmetic circuits and utilizing only logic functions. The approach creates compressed minimized implementations by converting trained WNN nodes from lookup tables to logic functions. The proposed LogicWiSARD is implemented in FPGA and ASIC technologies to illustrate its suitability for edge inference. Experimental results show more than 80% reduction in energy consumption when the proposed LogicWiSARD model is compared with a multilayer perceptron network (MLP) of equivalent accuracy. Compared to previous work on FPGA implementations for WNNs, convolutional neural networks, and binary neural networks, the energy savings of LogicWiSARD range between 32.2% and 99.6%. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Size | Format | |
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conferenceobject_91333.pdf | 438,33 kB | Adobe PDF | View/Open |
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