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Low-power 3D perception is useful in a wide range of computer-vision applications. Thanks to the increasing availability of high-resolution single-photon avalanche diode (SPAD) arrays, single-photon LiDARs (SPLs) have emerged as a promising technology for 3D sensing. The conventional image formation model for an SPL involves capturing the time-varying light intensity - which we call the transient distribution - of a reflected laser pulse in the form of an equi-width (EW) histogram. Unfortunately, this approach leads to unmanageable data rates (~gigabytes/second) with high-resolution arrays, severely limiting the applicability of SPLs in power- and bandwidth-constrained scenarios (e.g., mobile devices). The proposal introduces a different approach using race logic processing to construct equi-depth histograms with variable bin widths, which reduces the bandwidth requirement while maintaining similar ranging accuracy, showing bandwidth reduction of over 100× in simulations. 

Streaming applications from cluster monitoring to algorithmic trading deploy Kleene queries to detect and aggregate event trends. Rich event matching semantics determine how to compose events into trends. The expressive power of stateof- the-art streaming systems remains limited since they do not support many of these semantics. Worse yet, they suffer from long delays and high memory costs because they maintain aggregates at a fine granularity. To overcome these limitations, our Coarse-Grained Event Trend Aggregation (Cogra) approach supports a rich variety of event matching semantics within one system. Better yet, Cogra incrementally maintains aggregates at the coarsest granularity possible for each of these semantics. In this way, Cogra minimizes the number of aggregates – reducing both time and space complexity. Our experiments demonstrate that Cogra achieves up to six orders of magnitude speed-up and up to seven orders of magnitude memory reduction compared to state-of-the-art approaches. 

Streaming applications from algorithmic trading to traffic management deploy Kleene patterns to detect and aggregate arbitrarily-long event sequences, called event trends. State-of-the-art systems process such queries in two steps. Namely, they first construct all trends and then aggregate them. Due to the exponential costs of trend construction, this two-step approach su↵ers from both a long delays and high memory costs. To overcome these limitations, we propose the Graph-based Real-time Event Trend Aggregation (GRETA) approach that dynamically computes event trend aggregation without first constructing these trends. We define the GRETA graph to compactly encode all trends. Our GRETA runtime incrementally maintains the graph, while dynamically propagating aggregates along its edges. Based on the graph, the final aggregate is incrementally updated and instantaneously returned at the end of each query window. Our GRETA runtime represents a win-win solution, reducing both the time complexity from exponential to quadratic and the space complexity from exponential to linear in the number of events. Our experiments demonstrate that GRETA achieves up to four orders of magnitude speed-up and up to 50–fold memory reduction compared to the state-of-the-art two-step approaches.