{"ID":2866386,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19790","arxiv_id":"2509.19790","title":"Open-source Stand-Alone Versatile Tensor Accelerator","abstract":"Machine Learning (ML) applications demand significant computational resources, posing challenges for safety-critical domains like aeronautics. The Versatile Tensor Accelerator (VTA) is a promising FPGA-based solution, but its adoption was hindered by its dependency on the TVM compiler and by other code non-compliant with certification requirements. This paper presents an open-source, standalone Python compiler pipeline for the VTA, developed from scratch and designed with certification requirements, modularity, and extensibility in mind. The compiler's effectiveness is demonstrated by compiling and executing LeNet-5 Convolutional Neural Network (CNN) using the VTA simulators, and preliminary results indicate a strong potential for scaling its capabilities to larger CNN architectures. All contributions are publicly available.","short_abstract":"Machine Learning (ML) applications demand significant computational resources, posing challenges for safety-critical domains like aeronautics. The Versatile Tensor Accelerator (VTA) is a promising FPGA-based solution, but its adoption was hindered by its dependency on the TVM compiler and by other code non-compliant wi...","url_abs":"https://arxiv.org/abs/2509.19790","url_pdf":"https://arxiv.org/pdf/2509.19790v1","authors":"[\"Anthony Faure-Gignoux\",\"Kevin Delmas\",\"Adrien Gauffriau\",\"Claire Pagetti\"]","published":"2025-09-24T06:20:54Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}