Efficient and reconfigurable reservoir computing to realize alphabet pronunciation recognition based on processing-in-memory

• Main Authors: Liu, Shuang, Wu, Yuancong, Xiong, Canlong, Liu, Yihe, Yang, Jing, Yu, Q., Hu, S. G., Chen, Tupei, Liu, Y.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering; Computation Theory; Energy Efficiency
• Online Access: https://hdl.handle.net/10356/153571
• Institution: Nanyang Technological University

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.