Photonics-Enabled Edge Processing: A Vision for Near-Sensor Optical Intelligence

Edge intelligence is rapidly shifting computation from centralized cloud infrastructure toward the point of data generation. This shift is especially important for visual sensing systems, where continuous streams of high-dimensional pixel data must be converted, stored, transmitted, and processed under strict energy and latency constraints. While processing-in-sensor and processing-near-sensor architectures have reduced data movement, they remain limited by analog-to-digital conversion, memory access, electronic bandwidth, and the difficulty of supporting increasingly complex models near the sensor. This invited paper argues that integrated photonics can provide a new substrate for edge processing by enabling high-bandwidth, low-latency, and naturally parallel analog computation close to the sensing interface. We review the basic principles of photonic computing and discuss how it can be used to realize near-sensor multiply-and-accumulate operations. We then use recent work from our group as representative case studies, including optical in-sensor acceleration, optical near-sensor acceleration with compressive acquisition, near-sensor neuro-symbolic photonic computing, and in-sensor compressed weight retrieval for vision transformers. These examples motivate a broader research agenda in which photonics is not only a fast accelerator for neural operations, but also a system-level enabler for data-centric, energy-aware, and real-time edge intelligence.