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.1.
Extending the RISC-V ISA for efficient RNN-based 5G radio resource management / Andri R.; Henriksson T.; Benini L.. - ELETTRONICO. - 2020-:(2020), pp. 9218496.1-9218496.6. (Intervento presentato al convegno 57th ACM/IEEE Design Automation Conference, DAC 2020 tenutosi a usa nel 2020) [10.1109/DAC18072.2020.9218496].
Extending the RISC-V ISA for efficient RNN-based 5G radio resource management
Benini L.
2020
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.1.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
