We propose a novel architecture to enable low-power, complex on-node data processing, for the next generation of sensors for the internet of things (IoT), smartdust, or edge intelligence. Our architecture combines near-analog-memory-computing (NAM) and asynchronous-computing-with-streams (ACS), eliminating the need for ADCs. ACS enables ultra-low power, massive computational resources required to execute on-node complex Machine Learning (ML) algorithms; while NAM addresses the memory-wall that represents a common bottleneck for ML and other complex functions. In ACS an analog value is mapped to an asynchronous stream that can take one of two logic levels ( v h , v l ). This stream-based data representation enables area/power-efficient computing units such as a multiplier implemented as an AND gate yielding savings in power of ∼90% compared to digital approaches. The generation of streams for NAM and ACS in a brute force manner, using analog-to-digital-converters (ADCs) and digital-to-streams-converters, would sky-rocket the power-latency-energy cost making the approach impractical. Our NAM-ACS architecture eliminates expensive conversions, enabling an end-to-end processing on asynchronous streams data-path. We tailor the NAM-ACS architecture for random forest (RaF), an ML algorithm, chosen for its ability to classify using a reduced number of features. Simulations show that our NAM-ACS architecture enables 75% of savings in power compared with a single ADC, obtaining a classification accuracy of 85% using an RaF-inspired algorithm.

中文翻译：

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使用异步 Σ Δ 流实现超低功耗传感器的节点上机器学习

我们提出了一种新颖的架构，以实现低功耗、复杂的节点数据处理，用于下一代物联网 (IoT)、smartdust 或边缘智能传感器。我们的架构结合了近模拟内存计算 (NAM) 和带流的异步计算 (ACS)，无需 ADC。ACS 实现了执行节点上复杂机器学习 (ML) 算法所需的超低功耗、海量计算资源；而 NAM 解决了代表 ML 和其他复杂功能的常见瓶颈的内存墙。在 ACS 中，一个模拟值被映射到一个异步流，该流可以采用两个逻辑电平之一（v H ,v l ）。这种基于流的数据表示可以实现面积/功率高效的计算单元，例如实现为与门的乘法器，与数字方法相比，可节省约 90% 的功率。使用模数转换器 (ADC) 和数字流转换器以蛮力的方式为 NAM 和 ACS 生成流，将使功率-延迟-能量成本飙升，使该方法不切实际。我们的 NAM-ACS 架构消除了昂贵的转换，实现了异步流数据路径上的端到端处理。我们为随机森林 (RaF) 定制了 NAM-ACS 架构，这是一种 ML 算法，选择它是因为它能够使用数量减少的特征进行分类。仿真表明，与单个 ADC 相比，我们的 NAM-ACS 架构可节省 75% 的功耗，