Name
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 15x and 10x w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks.(1) |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DAC18072.2020.9218496 | PROCEEDINGS OF THE 2020 57TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC) |
Keywords | DocType | ISSN |
ASIP, RISC-V, Machine Learning, Neural Networks, RNN, LSTM | Conference | 0738-100X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renzo Andri | 1 | 87 | 6.44 |
| Henriksson Tomas | 2 | 0 | 0.34 |
| Luca Benini | 3 | 13116 | 1188.49 |