There is an emerging requirement for performing data-intensive parallel computations, e.g., machine-learning inference, locally on batteryless sensors. These devices are resource-constrained and operate intermittently due to the irregular energy availability in the environment. Intermittent execution might lead to several side effects that might prevent the correct execution of computational tasks. Even though recent studies proposed methods to cope with these side effects and execute these tasks correctly, they overlooked the efficient intermittent execution of parallelizable data-intensive machine-learning tasks. In this article, we present PiMCo—a novel programmable CRAM-based in-memory coprocessor that exploits the Processing In-Memory (PIM) paradigm and facilitates the power-failure resilient execution of parallelizable computational loads. Contrary to existing PIM solutions for intermittent computing, PiMCo promotes better programmability to accelerate a variety of parallelizable tasks. Our performance evaluation demonstrates that PiMCo improves the performance of existing low-power accelerators for intermittent computing by up to 8× and energy efficiency by up to 150×.

中文翻译：

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基于 CRAM 的可并行任务间歇计算加速

对于在无电池传感器上本地执行数据密集型并行计算（例如机器学习推理）出现了新的需求。这些设备资源有限，并且由于环境中能源可用性不规律而间歇性运行。间歇性执行可能会导致一些副作用，这些副作用可能会阻止计算任务的正确执行。尽管最近的研究提出了应对这些副作用并正确执行这些任务的方法，但他们忽略了可并行数据密集型机器学习任务的高效间歇执行。在本文中，我们介绍了 PiMCo——一种基于 CRAM 的新型可编程内存协处理器，它利用内存处理 (PIM) 范例，并促进可并行计算负载的电源故障弹性执行。与现有的间歇性计算 PIM 解决方案相反，PiMCo 提升了更好的可编程性，以加速各种可并行任务。我们的性能评估表明，PiMCo 将现有低功耗加速器的间歇计算性能提高了 8 倍，能效提高了 150 倍。