PYTHON-ENABLED DYNAMIC PARTIAL RECONFIGURATION FRAMEWORK FOR EFFICIENT FPGA-BASED CNN ACCELERATION

Abstract 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. Keywords: Field-Programmable Gate Array (FPGA), Dynamic Partial Reconfiguration (DPR), PYNQ Framework, Convolution Processing Unit, Edge Computing, Hybrid Classes, Python API, Reconfigurable Partition, Bitstream Management, Hardware Acceleration, Convolutional Neural Networks (CNN), Machine Learning Acceleration, Internet of Things (IoT), Real-time Processing, Embedded Systems