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arXiv · 2508.19656

Support Vector Machines Classification on Bendable RISC-V

Polykarpos Vergos·Theofanis Vergos·Florentia Afentaki·Konstantinos Balaskas·Georgios Zervakis
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Abstract

Flexible Electronics (FE) technology offers uniquecharacteristics in electronic manufacturing, providing ultra-low-cost, lightweight, and environmentally-friendly alternatives totraditional rigid electronics. These characteristics enable a rangeof applications that were previously constrained by the costand rigidity of conventional silicon technology. Machine learning (ML) is essential for enabling autonomous, real-time intelligenceon devices with smart sensing capabilities in everyday objects. However, the large feature sizes and high power consumption ofthe devices oppose a challenge in the realization of flexible ML applications. To address the above, we propose an open-source framework for developing ML co-processors for the Bendable RISC-V core. In addition, we present a custom ML accelerator architecture for Support Vector Machine (SVM), supporting both one-vs-one (OvO) and one-vs-rest (OvR) algorithms. Our ML accelerator adopts a generic, precision-scalable design, supporting 4-, 8-, and 16-bit weight representations. Experimental results demonstrate a 21x improvement in both inference execution time and energy efficiency, on average, highlighting its potential for low-power, flexible intelligence on the edge.

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Polykarpos Vergos, Theofanis Vergos, Florentia Afentaki, Konstantinos Balaskas, Georgios Zervakis. 2025-08-27. Support Vector Machines Classification on Bendable RISC-V. https://arxiv.org/abs/2508.19656

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