  Photonic computing has emerged as a promising solution for accelerating
computation-intensive artificial intelligence (AI) workloads, offering
unparalleled speed and energy efficiency, especially in resource-limited,
latency-sensitive edge computing environments. However, the deployment of
analog photonic tensor accelerators encounters reliability challenges due to
hardware noise and environmental variations. While off-chip noise-aware
training and on-chip training have been proposed to enhance the variation
tolerance of optical neural accelerators with moderate, static noise, we
observe a notable performance degradation over time due to temporally drifting
variations, which requires a real-time, in-situ calibration mechanism. To
tackle this challenging reliability issues, for the first time, we propose a
lightweight dynamic on-chip remediation framework, dubbed DOCTOR, providing
adaptive, in-situ accuracy recovery against temporally drifting noise. The
DOCTOR framework intelligently monitors the chip status using adaptive probing
and performs fast in-situ training-free calibration to restore accuracy when
necessary. Recognizing nonuniform spatial variation distributions across
devices and tensor cores, we also propose a variation-aware architectural
remapping strategy to avoid executing critical tasks on noisy devices.
Extensive experiments show that our proposed framework can guarantee sustained
performance under drifting variations with 34% higher accuracy and 2-3
orders-of-magnitude lower overhead compared to state-of-the-art on-chip
training methods. Our code is open-sourced at
https://github.com/ScopeX-ASU/DOCTOR.
