电池供电物联网设备的噪声感知DVFS转换序列优化

Shaoheng Luo, Cheng Zhuo, H. Gan
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引用次数: 7

摘要

低功耗片上系统(soc)现在是物联网(IoT)设备的核心,众所周知,物联网设备以其突发工作负载和有限的能量存储(通常以微型电池的形式)而闻名。为了确保电池寿命,DVFS已经成为这类SoC芯片的基本技术。随着供应水平的不断下降,这些设备的噪声边际已经被压缩了。在DVFS转换过程中,伴随着时钟速度转换的大电流在几个时钟周期内进入或流出时钟网络,并产生较大的Ldi/dt噪声,从而对PDN (power delivery network)造成压力。由于有限的面积和成本目标,增加额外的封头来减轻这种噪音通常是具有挑战性的。一种常见的方法是逐步引入/移除额外的时钟周期,以逐步增加或减少时钟频率,也称为时钟跳变。然而,这种技术可能会增加DVFS的转换时间,并且仍然不能保证最小的噪声。本文提出了一种新的噪声感知DVFS序列优化技术,通过制定混合0/1规划来解决时钟跳频序列优化问题。此外,该方法还被扩展到为同一目的在不同时钟域上调度大量唤醒活动。结果表明,与传统方法相比,该方法能够在期望的过渡时间内实现最小噪声序列,噪声降低53%,功耗节省15-17%以上。
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Noise-Aware DVFS Transition Sequence Optimization for Battery-Powered IoT Devices
Low power system-on-chips (SoCs) are now at the heart of Internet-of-Things (IoT) devices, which are well known for their bursty workloads and limited energy storage — usually in the form of tiny batteries. To ensure battery lifetime, DVFS has become an essential technique in such SoC chips. With continuously decreasing supply level, noise margins in these devices are already being squeezed. During DVFS transition, large current that accompanies the clock speed transition runs into or out of clock networks in a few clock cycles, and induces large Ldi/dt noise, thereby stressing the power delivery network (PDN). Due to the limited area and cost target, adding additional decap to mitigate such noise is usually challenging. A common approach is to gradually introduce/remove the additional clock cycles to increase or reduce the clock frequency in steps, a.k.a., clock skipping. However, such a technique may increase DVFS transition time, and still cannot guarantee minimal noise. In this work, we propose a new noise-aware DVFS sequence optimization technique by formulating a mixed 0/1 programming to resolve the problems of clock skipping sequence optimization. Moreover, the method is also extended to schedule extensive wake-up activities on different clock domains for the same purpose. The results show that we are able to achieve minimal-noise sequence within desired transition time with 53% noise reduction and save more than 15–17% power compared with the traditional approach.
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