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DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost140@linklings.com
SUMMARY:Resource-Efficient FPGA Pseudorandom Number Generation
DESCRIPTION:Posters, Research Posters\n\nResource-Efficient FPGA Pseudoran
 dom Number Generation\n\nCilasun, Peng, Gokhale\n\nProbability distributio
 ns play a critical role in diverse application domains. In simulations, pr
 obability distributions model phenomena such as physical properties of mat
 erials, of processes, or of behaviors. For instance, molecular dynamics co
 des often utilize the Maxwell-Boltzmann distribution for modeling temperat
 ure. Software-based random number generation usually employs rejection-bas
 ed approaches. Thus, sample generation is not guaranteed at each iteration
 . Many applications can be parallelized in hardware for acceleration purpo
 ses, yet rejection-based software methods do not fit well into FPGA fabric
  due to the complexity of exception circuitry. We introduce a resource-eff
 icient hardware pseudorandom number generator (RNG) and two optimizations:
  alias table partitioning and adaptive threshold resolution. The first tec
 hnique separates a target distribution into multiple sub-ranges and facili
 tates local optimizations in each sub-range to improve overall resource ut
 ilization. The second technique adjusts bit size for representing threshol
 d values to the precision of the underlying partition.\n\nRegistration Cat
 egory: Tech Program Reg Pass, Exhibits Reg Pass
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