Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management

Renzo Andri; Tomas Henriksson; Luca Benini

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arXiv · 2002.12877

Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management

Renzo Andri·Tomas Henriksson·Luca Benini

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Abstract

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$\times$ and 10$\times$ w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks.

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BibTeX RIS

Renzo Andri, Tomas Henriksson, Luca Benini. 2020-02-27. Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management. https://arxiv.org/abs/2002.12877

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