COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917388750946304 |
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| author | Chen, Boyang Khan, Mohd Tasleem Goussetis, George Sellathurai, Mathini Ding, Yuan Mota, João F. C. Lee, Jongeun |
| author_facet | Chen, Boyang Khan, Mohd Tasleem Goussetis, George Sellathurai, Mathini Ding, Yuan Mota, João F. C. Lee, Jongeun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques Chen, Boyang Khan, Mohd Tasleem Goussetis, George Sellathurai, Mathini Ding, Yuan Mota, João F. C. Lee, Jongeun Signal Processing I.2.7 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. |
| title | COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques |
| topic | Signal Processing I.2.7 |
| url | https://arxiv.org/abs/2510.03516 |