Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management
Authors:
- 1 July 2020
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Abstract
Radio Resource Management 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 coping 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. 1Keywords
All Related Versions
This publication has 20 references indexed in Scilit:
- Sustainable green networking: exploiting degrees of freedom towards energy-efficient 5G systemsWireless Networks, 2017
- Learning to optimize: Training deep neural networks for wireless resource management2017
- A reinforcement learning approach to power control and rate adaptation in cellular networks2017
- Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint DevicesIEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2017
- 5G Ultra-Dense Cellular NetworksIEEE Wireless Communications, 2016
- Human-level control through deep reinforcement learningNature, 2015
- The RISC-V Instruction Set Manual. Volume 1: User-Level ISA, Version 2.02014
- Optimal power allocation for green cognitive radio: fractional programming approachIET Communications, 2013
- An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel2011
- High Speed VLSI Implementation of the Hyperbolic Tangent Sigmoid Function2008