Efficient and reconfigurable reservoir computing to realize alphabet pronunciation recognition based on processing-in-memory
Shuang Liu, Yuancong Wu, Canlong Xiong, Yihe Liu, Yihe Liu, Jing Yang, Qi Yu, S. G. Hu, T. P. Chen, Y. Liu, Y. Liu · Applied Physics Letters · 2021
With its high energy efficiency and ultra-high speed, processing-in-memory (PIM) technology is promising to enable high performance in Reservoir Computing (RC) systems. In this work, we demonstrate an RC system based on an as-fabricated PIM chip platform. The RC system extracts input into a high-dimensional space through the nonlinear characteristic and randomly connected reservoir states inside the PIM-based RC. To examine the system, nonlinear dynamic system predictions, including nonlinear auto-regressive moving average equation of order 10 driven time series, isolated spoken digit recognition task, and recognition of alphabet pronunciation, are carried out. The system saves about 50% energy and requires much fewer operations as compared with the RC system implemented with digital logic. This paves a pathway for the RC algorithm application in PIM with lower power consumption and less hardware resource required.