ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2020
Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Periodic orbits in chaotic systems simulated at low precision.Scientific reports · 2023Article
- Stochastic rounding: implementation, error analysis and applications.Royal Society open science · 2022Review
- Fluid Simulations Accelerated With 16 Bits: Approaching 4x Speedup on A64FX by Squeezing ShallowWaters.jl Into Float16.Journal of advances in modeling earth systems · 2022Article
- EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems.Frontiers in neuroscience · 2022Article
- Auto-Selection of an Optimal Sparse Matrix Format in the Neuro-Simulator ANNarchy.Frontiers in neuroinformatics · 2022Article
- Beyond LIF Neurons on Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion.Sensors (Basel, Switzerland) · 2021Article
- Utilizing the Switching Stochasticity of HfOFrontiers in neuroscience · 2021Article
- Numerical algorithms for high-performance computational science.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Rethinking arithmetic for deep neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
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Authors and funding
4 authors.
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Abstract
Although double-precision floating-point arithmetic currently dominates high-performance computing, there is increasing interest in smaller and simpler arithmetic types. The main reasons are potential improvements in energy efficiency and memory footprint and bandwidth. However, simply switching to lower-precision types typically results in increased numerical errors. We investigate approaches to improving the accuracy of reduced-precision fixed-point arithmetic types, using examples in an important domain for numerical computation in neuroscience: the solution of ordinary differential equations (ODEs). The Izhikevich neuron model is used to demonstrate that rounding has an important role in producing accurate spike timings from explicit ODE solution algorithms. In particular, fixed-point arithmetic with stochastic rounding consistently results in smaller errors compared to single-precision floating-point and fixed-point arithmetic with round-to-nearest across a range of neuron behaviours and ODE solvers. A computationally much cheaper alternative is also investigated, inspired by the concept of
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