Jo, Yeji Mun, Kyusik Jeong, Yeonjoo Kwak, Joon Young Park, Jongkil Lee, Suyoun Kim, Inho Park, Jong-Keuk Hwang, Gyu-Weon Kim, Jaewook 2024-01-19T12:04:33Z 2024-01-19T12:04:33Z 2022-04-29 2022-04 2079-9292 https://pubs.kist.re.kr/handle/201004/115314 In this paper, we propose a novel Poisson process generator that uses multiple thermal noise amplifiers (TNAs) as a source of randomness and controls its event rate via a frequency-locked loop (FLL). The increase in the number of TNAs extends the effective bandwidth of amplified thermal noise and hence enhances the maximum event rate the proposed architecture can generate. Verilog-A simulation of the proposed Poisson process generator shows that its maximum event rate can be increased by a factor of 26.5 when the number of TNAs increases from 1 to 10. In order to realize parallel stochastic simulations of the biochemical reaction network, we present a fundamental reaction building block with continuous-time multiplication and addition using an AND gate and a 1-bit current-steering digital-to-analog converter, respectively. Stochastic biochemical reactions consisting of the fundamental reaction building blocks are simulated in Verilog-A, demonstrating that the simulation results are consistent with those of conventional Gillespie algorithm. An increase in the number of TNAs to accelerate the Poisson events and the use of digital AND gates for robust reaction rate calculations allow for faster and more accurate stochastic simulations of biochemical reactions than previous parallel stochastic simulators. English MDPI AG A Poisson Process Generator Based on Multiple Thermal Noise Amplifiers for Parallel Stochastic Simulation of Biochemical Reactions Article 10.3390/electronics11071039 1 ELECTRONICS, v.11, no.7 ELECTRONICS 11 7 Y scie scopus 000781333600001 Computer Science, Information Systems Engineering, Electrical & Electronic Physics, Applied Computer Science Engineering Physics Article CELL-CYCLE SYSTEMS ORIGINS Poisson process generator thermal noise amplifier AND gate stochastic simulation biochemical reactions