Cloud-Offloaded Logic Synthesis for FPGAs with Limited Resources

Abstract

Field-Programmable Gate Arrays (FPGAs) have become essential for implementing energy-efficient and low-latency solutions in domains ranging from embedded systems to AI acceleration. However, resource-constrained FPGAs—commonly used in edge and IoT devices—face limitations in computational capacity, memory availability, and logic density. These constraints hinder the feasibility of running complex logic synthesis and place-and-route flows locally. This paper proposes a cloud-offloaded logic synthesis framework that shifts computationally intensive stages of FPGA design to the cloud, enabling real-time and on-demand compilation of custom logic functions for devices with minimal resources. The proposed system architecture integrates a lightweight client-side front end, responsible for high-level HDL parsing and resource estimation, with a scalable back end hosted on cloud infrastructure. The cloud service performs synthesis, mapping, placement, and routing, and returns partial or full bitstreams tailored for specific FPGA configurations. A secure communication protocol ensures the integrity and confidentiality of the design throughout transmission. To minimize latency, a caching mechanism is implemented for frequently used modules, while bitstream compression techniques reduce transmission overhead. Empirical evaluation was conducted on Xilinx Artix-7 and Lattice iCE40 FPGAs using a range of benchmark designs, including DSP filters, machine learning kernels, and sensor controllers. Results show a reduction of 70% in local compilation time and a 50% improvement in logic utilization efficiency due to access to more powerful optimization algorithms in the cloud. Furthermore, the system supports dynamic partial reconfiguration, allowing real-time hardware updates without interrupting core operations. This study demonstrates that cloud-offloaded logic synthesis is a viable and efficient solution for extending the capabilities of resource-limited FPGAs, unlocking a broader range of use cases for edge computing, real-time signal processing, and reconfigurable IoT systems.