Mobile computing perspective on event cameras identifies the abstraction layers that are missing

Event Camera Meets Resource-Aware Mobile Computing: Abstraction, Algorithm, Acceleration, Application (arxiv 2503.22943) takes a systems perspective on event cameras that complements the computer vision literature: rather than presenting a new algorithm, it analyzes the full stack of software and hardware layers needed to build practical embedded systems around event cameras, and identifies where the stack is missing.

The abstraction layer gap

The paper argues that event cameras lack a stable software abstraction that sits between raw hardware events and application algorithms — equivalent to the camera HAL (hardware abstraction layer) that stabilizes conventional camera development.

When a conventional camera is integrated into a mobile system, developers access it through a standardized interface that handles sensor initialization, bias calibration, frame synchronization, and data buffering. The interface is the same regardless of whether the underlying camera is from Sony, OmniVision, or Samsung.

Event cameras have no such abstraction. Each sensor family (Prophesee, Samsung, Sony, iniVation) has a different event format, different bias configuration interface, different streaming protocol, and different triggering mechanism. Algorithm development requires sensor-specific code that does not transfer cleanly to other sensors.

The paper proposes a minimal event camera abstraction specification that captures what all event cameras have in common and provides a portable interface for algorithm development.

Algorithm requirements for mobile platforms

The mobile computing framing highlights requirements that academic benchmarks do not capture:

Memory constraints: mobile platforms (smartphones, edge computers, embedded flight computers) have SRAM measured in kilobytes or megabytes, not the gigabytes assumed by most neural network implementations. Event-camera algorithms that require large feature maps or large weight tensors are not mobile-compatible without significant modification.

Mixed workloads: a mobile platform running event-camera processing is also running other tasks — sensor fusion, communications, navigation. The event-camera pipeline must coexist with these workloads without starvation or interference.

Dynamic power: mobile platforms adjust clock speed and power consumption based on workload. Algorithms that assume a fixed clock rate fail to account for the variable timing introduced by dynamic voltage-frequency scaling.

Acceleration results

The paper presents implementations of several event-camera algorithms on mobile SoCs and FPGAs, demonstrating that thoughtful implementation choices can achieve 5–20x speedups over naive implementations running on the same hardware. The most impactful optimizations are memory layout (keeping working sets in on-chip SRAM rather than DRAM), sparse computation (skipping zero-valued event contributions), and pipeline overlap (processing events while new ones are being received).

Relevance to seeker packaging

The mobile computing framing is closer to the seeker problem than the robotics framing: both involve embedded SoC-class processors, power budgets measured in watts rather than tens of watts, and integration with other systems through constrained interfaces. The abstraction layer analysis and the algorithm requirements catalog are immediately applicable to seeker architecture planning.

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Event Camera Meets Resource-Aware Mobile Computing: Abstraction, Algorithm, Acceleration, Application. arxiv 2503.22943