论文标题
将RISC-V ISA扩展为有效的基于RNN的5G无线电资源管理
Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management
论文作者
论文摘要
5G移动通信中的无线电资源管理（RRM）是一个充满挑战的问题，重复的神经网络（RNN）表现出了令人鼓舞的结果。因此，加速计算密集型RNN推断至关重要。对于有效的5G-RRM顶部应对RNN变化的景观，可编程解决方案是可行的。在本文中，我们通过调整微型控制器级开源RISC-V核心的指令集和微体系结构来研究RNN推理加速度。我们将HW扩展名与软件优化相结合，以实现15 $ \ times $和10 $ \ times $ w.r.t.的吞吐量和能源效率的总体提高。在各种RRM任务中使用的各种RNN的基线核心。
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.
