Energy harvesting is an attractive way to power future Internet of Things (IoT) devices since it can eliminate the need for battery or power cables. However, harvested energy is intrinsically unstable. While Field-programmable Gate Array (FPGAs) have been widely adopted in various embedded systems, it is hard to survive unstable power since all the memory components in FPGA are based on volatile Static Random-access Memory (SRAMs). The emerging non-volatile memory-based FPGAs provide promising potentials to keep configuration data on the chip during power outages. Few works have considered implementing efficient runtime intermediate data checkpoint on non-volatile FPGAs. To realize accumulative computation under intermittent power on FPGA, this article proposes a low-cost design framework, Data-Flow-Tracking FPGA (DFT-FPGA), which utilizes binary counters to track intermediate data flow. Instead of keeping all on-chip intermediate data, DFT-FPGA only targets on necessary data that is labeled by off-line analysis and identified by an online tracking system. The evaluation shows that compared with state-of-the-art techniques, DFT-FPGA can realize accumulative computing with less off-line workload and significantly reduce online roll-back time and resource utilization.

中文翻译：

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间歇供电非易失性 FPGA 的低开销在线数据流跟踪

能量收集是为未来物联网 (IoT) 设备供电的一种有吸引力的方式，因为它可以消除对电池或电源线的需求。然而，收集的能量本质上是不稳定的。虽然现场可编程门阵列 (FPGA) 已广泛应用于各种嵌入式系统，但由于 FPGA 中的所有存储器组件均基于易失性静态随机存取存储器 (SRAM)，因此难以承受不稳定的电源。新兴的基于非易失性存储器的 FPGA 提供了在断电期间将配置数据保存在芯片上的潜力。很少有工作考虑在非易失性 FPGA 上实现高效的运行时中间数据检查点。为了在FPGA上实现间歇性电源下的累积计算，本文提出了一种低成本的设计框架Data-Flow-Tracking FPGA (DFT-FPGA)，它利用二进制计数器来跟踪中间数据流。DFT-FPGA 不保留所有片上中间数据，而是仅针对通过离线分析标记并由在线跟踪系统识别的必要数据。评估表明，与最先进的技术相比，DFT-FPGA能够以较少的离线工作量实现累积计算，并显着降低在线回滚时间和资源利用率。