OS-ELM数字电路无上溢/下溢定点位宽优化方法
An Overflow/Underflow-Free Fixed-Point Bit-Width Optimization Method for OS-ELM Digital Circuit
摘要：
目前，对资源有限的物联网设备（如智能传感器）的实时培训需求越来越大，智能传感器实现了对流数据的独立在线适应，而无需将数据传输到远程服务器。OS-ELM（Online Sequential Extreme Learning Machine）是一种基于神经网络的在线学习算法，具有计算量小、易于数字电路实现等优点。现有的OS-ELM数字电路采用定点数据格式，并且通常手动调整位宽，然而，这可能导致上溢或下溢，这可能导致电路的意外行为。对于片上学习系统，无上溢/下溢设计具有很大的影响，因为在线训练是连续进行的，并且中间变量的间隔将随着时间的推移而动态变化。本文提出了一种适用于OS-ELM定点数字电路的无上溢/下溢位宽优化方法。实验结果表明，我们的方法实现了无上溢/下溢OS-ELM数字电路与1.0倍-1.5倍以上的面积成本相比，基线模拟方法，上溢或下溢可能发生。
英文摘要：
Currently there has been increasing demand for real-time training on resource-limited IoT devices such as smart sensors, which realizes standalone online adaptation for streaming data without data transfers to remote servers. OS-ELM (Online Sequential Extreme Learning Machine) has been one of promising neural-network-based online algorithms for on-chip learning because it can perform online training at low computational cost and is easy to implement as a digital circuit. Existing OS-ELM digital circuits employ fixed-point data format and the bit-widths are often manually tuned, however, this may cause overflow or underflow which can lead to unexpected behavior of the circuit. For on-chip learning systems, an overflow/underflow-free design has a great impact since online training is continuously performed and the intervals of intermediate variables will dynamically change as time goes by. In this paper, we propose an overflow/underflow-free bit-width optimization method for fixed-point digital circuits of OS-ELM. Experimental results show that our method realizes overflow/underflow-free OS-ELM digital circuits with 1.0x - 1.5x more area cost compared to the baseline simulation method where overflow or underflow can happen.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
