Radio Resource Management (RRM) in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results. Accelerating the compute-intensive RNN inference is therefore of utmost importance. Programmable solutions are desirable for effective 5G-RRM top cope with the rapidly evolving landscape of RNN variations. In this paper, we investigate RNN inference acceleration by tuning both the instruction set and micro-architecture of a micro-controller-class open-source RISC-V core. We couple HW extensions with software optimizations to achieve an overall improvement in throughput and energy efficiency of 15× and 10× w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks.
@article{arxiv.2002.12877,
title = {Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management},
author = {Renzo Andri and Tomas Henriksson and Luca Benini},
journal= {arXiv preprint arXiv:2002.12877},
year = {2020}
}