Abstract
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 functionality 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. Performing SR purely in software can be expensive due to requirement of multiple masking and shifting instructions, and an addition operation per each rounding. Also, saturation of values is included since it usually follows rounding, which is common in fixed-point arithmetic due to a narrow dynamic range. 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. The proposed accelerator is not specific to SpiNNaker, and is a generally applicable rounding unit provided a pseudo-random number generator is available that can supply random bits to it. Additionally, to the best of our knowledge, this is the first exploration of a rounding accelerator with a programmable bit position and multiple data type support.
Original language | English |
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Title of host publication | International Joint Conference on Neural Networks 2021 |
Publisher | IEEE |
DOIs | |
Publication status | Published - 20 Sept 2021 |
Event | International Joint Conference on Neural Networks 2021 - Duration: 18 Jul 2021 → 22 Jul 2021 |
Conference
Conference | International Joint Conference on Neural Networks 2021 |
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Abbreviated title | IJCNN2021 |
Period | 18/07/21 → 22/07/21 |