Yow Iwaoka

FPGA Implementation of a Data-Driven Stochastic Biochemical Simulator with the Next Reaction Method

This paper introduces a scalable FPGA implementation of a stochastic simulation algorithm (SSA) called the next reaction method. There are some hardware approaches of SSAs that obtained high-throughput on reconfigurable devices such as FPGAs, but these works lacked in scalability. The design of this work can accommodate to the increasing size of target biochemical models, or to make use of increasing capacity of FPGAs. Interconnection network between arithmetic circuits and multiple simulation circuits aims to perform a data-driven multi-threading simulation. Approximately 8 times speedup was obtained compared to an execution on Xeon 2.80 GHz.

The design of scalable stochastic biochemical simulator on FPGA

Biochemical simulations including whole-cell models require high performance computing systems. Reconfigurable systems are expected to be an alternative solution for conventional methods by PC clusters or vector computers. This paper shows the implementation of a stochastic biochemical simulation algorithm called Next Reaction Method for Virtex-II PRO. As the result of benchmarking with a small reaction system, the FPGA-based simulator outperforms the software implementation on Xeon 2.40 GHz by 17.1 times

Efficient scheduling of rate law functions for ODE-based multimodel biochemical simulation on an FPGA

A reconfigurable biochemical simulator by solving ordinary differential equations has received attention as a personal high speed environment for biochemical researchers. For efficient use of the reconfigurable hardware, static scheduling of high-throughput arithmetic pipeline structures is essential. This paper shows and compares some scheduling alternatives, and analyzes the tradeoffs between performance and hardware amount. Through the evaluation, it is shown that the sharing first scheduling reduces the hardware cost by 33.8% in average, with the up to 11.5% throughput degradation. Effects of sharing of rate law functions are also analyzed.

A framework for ODE-based multimodel biochemical simulations on an FPGAs

Today, mathematical modeling and simulation of biochemical pathways take a major role in biological researches. However, modern microprocessors cannot provide enough throughputs to explore the large parameter space of target pathways. To address this problem, ReCSiP (a reconfigurable cell simulation platform), an FPGA-based biochemical simulator is proposed. Its an ODE-based simulator, which solves the rate-law functions. The framework proposed in this paper, enables to simulate pathways consisting many different types of chemical reactions by connecting the rate-law modules (solvers) on an FPGA. It provides the solver-to-solver communication mechanism on an FPGA and automatic configuration software to generate the circuit.

A Combining Technique of Rate Law Functions for a Cost-Effective Reconfigurable Biological Simulator

In order to simulate large scale biological models with a reconfigurable FPGA-based biochemical simulator system, reduction of required resources are essential. This paper proposes a method which combines common terms in rate law functions appeared in biochemical models and generates a shared hardware module used for numerical integration. In this approach, two functions are combined in a tree structure level, followed by pipeline scheduling and arithmetic module binding. The evaluation result reveals that this approach reduces hardware resources by 31.4 % on average at the cost of 14.4 % throughput degradation.