Gene regulatory accelerators on cloud FPGA

Gene regulatory networks (GRN) are dynamic models in time and space. These models are used to predict diseases and in drugs research. GRN models are discrete, and Boolean graphs can efficiently represent them. However, GRN algorithms explore a large solution space with high computational complexity. This work proposes efficient FPGA-based accelerators to implement two GRN algorithms: attractor computation and Derrida plot. Nevertheless, FPGA accelerator design and deployment are still a challenge. This work presents an accelerator design framework for AWS Amazon FPGA cloud. The framework simplifies the software (SW) and hardware (HW) generation for GRN accelerators. The user provides a high-level model for the Boolean GRN, and our tool automatically creates AWS-ready-to-deploy software and hardware components. For the attractor and the Derrida plot computation, the proposed FPGA accelerators are on average 44x$$ 44\times $$ and 18x$$ 18\times $$ faster than a V100 GPU.