The Objectives of WP1 are:
1) Design scalable parallel architectures for accelerating Gillespie’s stochastic simulation algorithms (both the FRM-SSA and NRM-SSA)
2) Implementation of the designed multiprocessor SoC architectures using FPGAs.
3) Validation and comparison using biomodels or increasing complexity.
Development of flexible parametric IP cores for synthesizing SSA implementations for FPGAs
The research of WP1 opens up a new direction in that it enables, through the soft IP cores we developed, the flexible rapid prototyping of optimal Systems on Chip, tailored to the stochastic simulation needs of the biomodel at hand. This flexibility is important since it allows synthesizing area and energy efficient SoC instances that can be adjusted to match the capabilities of different FPGA devices. SoCs for embedded simulation systems can also be designed if we need to incorporate them as components into larger computational workflows or specialized instruments. In general, our IP cores can be thought as reconfigurable hardware system generators that can be used flexibly to produce SoC instances for the FRM-SSA and NRM-SSA algorithms suitable for a wide spectrum of different use scenarios. These range from the realization of a small networks (e.g. for synthetic biology) for small FPGA devices attached to a laptop, to whole cellular subsystem (with many interacting pathways) simulated with a larger FPGA attached to server host. Such flexibility in creating Systems on Chip (SoC) for systems biology is not available today, even though it is essential for minimizing hardware design time and allowing the scientific community to take advantage of widely available powerful computing devices (FPGAs) of rapidly dropping cost.
The Deliverables (technical reports) of WP1 are:
D1.1 Parameterized HDL SoC descriptions for the FRM-SSA
D1.2 Parameterized HDL SoC descriptions for the NRM-SSA
D1.3 Validation of the derived FPGA implementations using biomodels of increasing complexity
Publications for WP1
Evangelos Koutsouradis, George Provelengios, Elias Kouskoumvekakis, Elias S. Manolakos:
Scalable FPGA Accelerator of the NRM Algorithm for Efficient Stochastic Simulation of Large-Scale Biochemical Reaction Networks. In Preeceedings of 2015 Euromicro Conference on Digital System Design, Madeira, Portugal, August 26-28, 2015. IEEE Computer Society 2015, ISBN 978-1-4673-8035-5, DSD2015: 583-590