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.

中文翻译：

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基于内存处理的高效可重构水库计算实现字母发音识别

凭借其高能效和超高速，内存处理 (PIM) 技术有望在水库计算 (RC) 系统中实现高性能。在这项工作中，我们展示了一个基于预制 PIM 芯片平台的 RC 系统。RC 系统通过基于 PIM 的 RC 内部的非线性特征和随机连接的储层状态将输入提取到高维空间中。为了检验该系统，进行了非线性动态系统预测，包括 10 阶驱动时间序列的非线性自回归移动平均方程、孤立的口语数字识别任务和字母发音的识别。与使用数字逻辑实现的 RC 系统相比，该系统可节省约 50% 的能源，并且需要的操作更少。