Stochastic Rounding: Algorithms and Hardware Accelerator
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Abstract (arXiv)
Algorithms and a hardware accelerator for performing stochastic rounding (SR) are presented. The main goal is to augment the ARM M4F based multi-core processor SpiNNaker2 with a more flexible rounding functionality than is available in the ARM processor itself. The motivation of adding such an accelerator in hardware is based on our previous results showing improvements in numerical accuracy of ODE solvers in fixed-point arithmetic with SR, compared to standard round to nearest or bit truncation rounding modes. Furthermore, performing SR purely in software can be expensive, due to requirement of a pseudorandom number generator (PRNG), multiple masking and shifting instructions, and an addition operation. Also, saturation of the rounded values is included, since rounding is usually followed by saturation, which is especially important in fixed-point arithmetic due to a narrow dynamic range of representable values. The main intended use of the accelerator is to round fixed-point multiplier outputs, which are returned unrounded by the ARM processor in a wider fixed-point format than the arguments.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2022On the influence of stochastic roundoff errors and their bias on the convergence of the gradient descent method with low-precision floating-point computationcited by 4
- 2021A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precisioncited by 3
- 2025FeNN: A RISC-V vector processor for Spiking Neural Network accelerationcited by 1
- 2026The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computingcited by 0
- 2026The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing27 Jul 2026
- 2025FeNN: A RISC-V vector processor for Spiking Neural Network acceleration13 Jun 2025
- 2022On the influence of stochastic roundoff errors and their bias on the convergence of the gradient descent method with low-precision floating-point computation24 Feb 2022
- 2021A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision24 Mar 2021