{"ID":2861162,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03516","arxiv_id":"2510.03516","title":"COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques","abstract":"Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift-accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques -- parallel, shared, split, and hybrid -- and evaluate their efficiency. Building on this foundation, we develop a general matrix multiplication core using the im2col transformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.","short_abstract":"Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performan...","url_abs":"https://arxiv.org/abs/2510.03516","url_pdf":"https://arxiv.org/pdf/2510.03516v3","authors":"[\"Boyang Chen\",\"Mohd Tasleem Khan\",\"George Goussetis\",\"Mathini Sellathurai\",\"Yuan Ding\",\"João F. C. Mota\",\"Jongeun Lee\"]","published":"2025-10-03T21:02:34Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}