Hardware-Algorithm Co-Design of a Compressed Fuzzy Active Learning Method
| Autor: | Ramtin Zand, Sajad Haghzad Klidbary, Ehsan Jokar, Arash Ahmadi, Saeed Bagheri Shouraki, Hadis Abolfathi |
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| Rok vydání: | 2020 |
| Předmět: |
Soft computing
Computer science 020208 electrical & electronic engineering Feature extraction Inference 02 engineering and technology Digital architecture Fuzzy logic Computer engineering Control system Hardware algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering Cluster analysis |
| Zdroj: | IEEE Transactions on Circuits and Systems I: Regular Papers. 67:4932-4945 |
| ISSN: | 1558-0806 1549-8328 |
| DOI: | 10.1109/tcsi.2020.3010723 |
| Popis: | Active learning method (ALM) is a powerful fuzzy–based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real–time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from $O(N^{2})$ to $O(2N)$ for a multi–input single–output (MISO) system. Also, the computational costs in both training and inference modes are decreased to only a few additions and multiplications. Furthermore, we develop a memory–efficient digital architecture for the proposed compressed ALM algorithm that can be leveraged for various computing systems through configuring a few registers. Finally, we assess the performance of the proposed approach using various function modeling and classification applications and provide a comparison with conventional ALM and some other well-know approaches. Simulation and hardware implementation results demonstrate that the proposed approach achieves reduced noise sensitivity with $128\times $ reduction in the average memory usage while realizing comparable accuracy compared to the other approaches studied herein. |
| Databáze: | OpenAIRE |
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