DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Accelerators

• URL: http://arxiv.org/abs/2403.02688v2
• Date: Fri, 31 May 2024 20:24:47 GMT
• Title: DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Accelerators
• Authors: Haotian Lu, Sanmitra Banerjee, Jiaqi Gu,
• Abstract summary: Photonic tensor accelerators offer unparalleled speed and energy efficiency. Off-chip noise-aware training and on-chip training have been proposed to enhance the variation tolerance of optical neural accelerators. We propose a lightweight dynamic on-chip framework, dubbed DOCTOR, providing adaptive, in-situ accuracy recovery against temporally drifting noise.
• Score: 5.873308516576125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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