Extending the intelligence of sensors to the data-acquisition process - deciding whether to sample or not - can result in transformative energy-efficiency gains. However, making such a decision in a deterministic manner involves risk of losing information. Here we present a sensing paradigm that enables making such a decision in a probabilistic manner. The paradigm takes inspiration from the autonomous nervous system and employs a probabilistic neuron (p-neuron) driven by an analog feature extraction circuit. The response time of the system is on the order of microseconds, over-coming the sub-sampling-rate response time limit and enabling real-time intelligent autonomous activation of data-sampling. Validation experiments on active seismic survey data demonstrate lossless probabilistic data acquisition, with a normalized mean squared error of 0.41%, and 93% saving in the active operation time of the system and the number of generated samples.

翻译：将传感器的智能扩展至数据采集过程——决定是否采样——能够带来变革性的能效提升。然而，以确定性的方式做出此类决策存在信息丢失的风险。本文提出一种感知范式，使得此类决策能够以概率方式进行。该范式受自主神经系统启发，采用由模拟特征提取电路驱动的概率神经元（p-neuron）。系统的响应时间在微秒量级，突破了次采样率响应时间的限制，实现了实时、智能、自主的数据采样激活。在主动地震勘测数据上进行的验证实验表明，该方案实现了无损的概率数据采集，归一化均方误差为0.41%，同时使系统的有效运行时间与生成样本数量减少了93%。