{"ID":2877524,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19656","arxiv_id":"2508.19656","title":"Support Vector Machines Classification on Bendable RISC-V","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.19656","url_pdf":"https://arxiv.org/pdf/2508.19656v1","authors":"[\"Polykarpos Vergos\",\"Theofanis Vergos\",\"Florentia Afentaki\",\"Konstantinos Balaskas\",\"Georgios Zervakis\"]","published":"2025-08-27T08:06:11Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}