PYTHON-ENABLED DYNAMIC PARTIAL RECONFIGURATION FRAMEWORK FOR EFFICIENT FPGA-BASED CNN ACCELERATION – Volume 12 Issue 5

Field-Programmable Gate Arrays (FPGAs) offer significant potential for accelerating machine learning workloads in edge computing applications, yet their adoption remains limited due to programming complexity and inflexible resource management. While existing frameworks like Xilinx PYNQ simplify FPGA programming through Python APIs, they lack native support for dynamic partial reconfiguration (DPR), resulting in inefficient resource utilization and prolonged reconfiguration times that hinder real-time applications. This research addresses these critical limitations by introducing the ‘pynqpartial’ package, a novel extension to the PYNQ framework that seamlessly integrates DPR capabilities with high-level Python programming interfaces through hybrid classes that combine software and hardware functionality, enabling transparent management of partial bitstreams for convolution neural network applications. The methodology involves implementing a dedicated convolution processing unit on a PYNQ-Z2 FPGA platform that supports dynamic switching between multiple precision levels (8, 16, and 32-bit integer operations) and various kernel configurations, with system architecture consisting of static regions maintaining core functionality and reconfigurable partitions where convolution modules are dynamically loaded based on application requirements. Implementation utilized Vivado for hardware synthesis and Python through Jupyter Notebook for runtime control, with comprehensive validation performed on timing performance, resource utilization, and functional correctness across different operational scenarios. Experimental results demonstrate remarkable performance improvements, achieving 800× faster reconfiguration compared to traditional full bitstream methods while maintaining successful timing closure at 100 MHz operation, with resource utilization analysis revealing efficient allocation where reconfigurable modules consume less than 20% of available lookup tables, ensuring reliable dynamic reconfiguration with minimal overhead during partial reconfiguration operations, making the system suitable for real-time CNN workloads in IoT and edge devices, significantly enhancing FPGA accessibility for software developers while providing substantial performance benefits for next-generation edge AI applications.

This work presented a Dynamic Partial Reconfiguration (DPR)-enabled Convolution Processing Unit (CPU) for the Xilinx PYNQZ2 platform, designed to accelerate convolutional neural network (CNN) workloads on resource-constrained edge devices. By introducing the pynqpartial Python package and a hybrid class abstraction, the proposed system bridges the gap between low-level FPGA reconfiguration and high-level Python-based application development. The architecture partitions the FPGA fabric into a static region and a reconfigurable partition, enabling runtime swapping of convolution modules with varying kernel sizes and precision levels. Experimental results demonstrated: Up to 800× reduction in reconfiguration time compared to full bitstream loading. ~70% lower LUT usage versus static multi-core designs, freeing resources for additional accelerators. 6.2× throughput improvement over software-only execution on the ARM cores. Stable operation at 100 MHz across all reconfigurable modules. These results confirm that DPR, when integrated with a user-friendly API, can deliver both performance gains and design flexibility for adaptive edge AI applications.

[1] Gu, J., Wang, H., Guo, X., Schulz, M., & Gerndt, M. (2025). VersaSlot: Efficient fine-grained FPGA sharing with Big.Little slots and live migration in FPGA clusters. arXiv Preprint arXiv:2503.05930. [2] Coyote v2: Raising the level of abstraction for data center FPGA services. (2025). arXiv Preprint arXiv:2504.21538. [3] Hota, A., Xiao, Y., Park, D., & DeHon, A. (2022). HiPR: High-level partial reconfiguration for fast incremental FPGA compilation. ACM Transactions on Reconfigurable Technology and Systems, 16(4), 1–26. [4] Park, D., Xiao, Y., & DeHon, A. (2022). Fast and flexible FPGA development using hierarchical partial reconfiguration. In Proceedings of the International Conference on Field-Programmable Technology (FPT) (pp. 1–9). IEEE. [5] Fuchs, M., Rech, P., Herkersdorf, A., Sterpone, L., & Wirthlin, M. (2024). Cross-chip partial reconfiguration for the initialization of modular and scalable heterogeneous systems. IEEE Transactions on Nuclear Science. Advance online publication. [6] Phani, T. S. S. P., Arumalla, A., Prakash, D. M., & Memisevic, L. (2021). Partial dynamic reconfiguration framework for FPGA: A survey with concepts, constraints and trends. Materials Today: Proceedings, 49, 2766–2773. [7] Boudjadar, J. (2025). Dynamic FPGA reconfiguration for scalable embedded convolutional neural networks. Future Generation Computer Systems, 163, 199–210. [8] Syed, R. T., Gilani, S. A. M., Khan, M. R., Qamar, A., Malik, A. S., & Ayub, N. M. N. (2024). FPGA implementation of a fault-tolerant fused and branched CNN accelerator with reconfigurable capabilities. IEEE Access, 12, 57847–57863. [9] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097–1105). [10] Chen, Y. H., Krishna, T., Emer, J., & Sze, V. (2017). Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE Journal of Solid-State Circuits, 52(1), 127–138. [11] Sharma, H., Park, J., Mahajan, D., Amaro, E., Kim, J. K., Lymberopoulos, C., Tsotras, P. A., & Kim, N. S. (2016). From high-level deep neural models to FPGAs. In Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) (pp. 1–12). IEEE. [12] Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., & Cong, J. (2015). Optimizing FPGA-based accelerator design for deep convolutional neural networks. In Proceedings of the ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) (pp. 161–170). ACM. [13] Fowers, J., Ovtcharov, K., Papamichael, M., Massengill, T., Liu, M., Lo, D., Alkalay, S., & Chung, E. (2018). A configurable cloud-scale DNN processor for real-time AI. In Proceedings of the 45th Annual International Symposium on Computer Architecture (ISCA) (pp. 1–14). IEEE. [14] Xilinx Inc. (n.d.). PYNQ: Python productivity for Zynq. Retrieved September 11, 2025, from https://pynq.io [15] Bommana, S. R., Karunakaran, K., Reddy, B. B., & Shankar, A. (2025). Mitigating side-channel attacks on FPGA through dynamic partial reconfiguration. PLOS ONE, 20(4), e0300000. [16] Jain, A. K., Khalid, M. A., & Arslan, T. (2021). FPGA overlays: A survey of techniques and tools. IEEE Access, 9, 139–158. [17] Boudjadar, J. (2025). Dynamic FPGA reconfiguration for scalable embedded convolutional neural networks. Future Generation Computer Systems, 163, 199–210. [18] Jain, A. K., Khalid, M. A., & Arslan, T. (2021). FPGA overlays: A survey of techniques and tools. IEEE Access, 9, 139–158.

Call for Paper (Hub) Fast Paper Publication IJCT Indexing & Databases Submit Manuscript Call for Paper: Sep–Oct 2025 Call for Paper: Publish Research