Resilient Computing with Dynamical Systems
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
We reformulate fundamental numerical problems to run on novel hardware inspired by the brain. Such "neuromorphie hardware consumes less energy per computation, promising a means to augment next-generation exascale computers. However, their programming model is radically different from floating-point machines, with fewer guarantees about precision and communication. The approach is to pass each given problem through a sequence of transformations (algorithmic "reductions") which change it from conventional form into a dynamical system, then ultimately into a spiking neural network. Results for the eigenvalue problem are presented, showing that the dynamical system formulation is feasible.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1569151
- Report Number(s):
- SAND-2019-10963; 679662
- Country of Publication:
- United States
- Language:
- English
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