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.
翻译:目前,对资源有限的IOT设备(如智能传感器)的实时培训需求不断增长,智能传感器等资源有限的IOT设备实现了对流数据的独立在线适应,而没有数据传输到远程服务器。 OS-ELM(在线序列极端学习机)一直是充满希望的线上学习神经网络在线算法之一,因为它可以以低计算成本进行在线培训,并且很容易作为数字电路实施。现有的OS-ELM数字电路采用固定点数据格式,而比特维经常手工调整,但这可能导致流出或下流,从而导致电路出现意外行为。对于在网上学习系统来说,溢出/下流设计具有很大影响,因为不断进行在线培训,中间变量的间隔随着时间推移而会发生动态变化。在本文中,我们建议对OS-ELM的固定点数字电路进行溢出/下流无位优化方法,而S-ELM 实验结果显示,我们的方法可以实现溢出/下流流流成本,从而导致电路路的意外行为。