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.

Related papers

• On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise [0.0]
  This paper presents a memristor-based compute-in-memory hardware accelerator for on-chip training and inference. Hardware, consisting of 30 memristors and 4 neurons, utilizes three different M-SDC structures with tungsten, chromium, and carbon media to perform binary image classification tasks.
  arXiv  Detail & Related papers  (2024-08-26T23:10:01Z)
• Taming 3DGS: High-Quality Radiance Fields with Limited Resources [50.92437599516609]
  3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with its fast, interpretable, and high-fidelity rendering. We tackle the challenges of training and rendering 3DGS models on a budget. We derive faster, numerically equivalent solutions for gradient computation and attribute updates.
  arXiv  Detail & Related papers  (2024-06-21T20:44:23Z)
• PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search [64.28335667655129]
  Multiple object tracking is a critical task in autonomous driving. As tracking accuracy improves, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency. In this paper, we explore the use of the neural architecture search (NAS) methods to search for efficient architectures for tracking, aiming for low real-time latency while maintaining relatively high accuracy.
  arXiv  Detail & Related papers  (2024-03-23T04:18:49Z)
• Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions [15.613528945524791]
  This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults.
  arXiv  Detail & Related papers  (2023-11-30T13:30:00Z)
• Cal-DETR: Calibrated Detection Transformer [67.75361289429013]
  We propose a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR, UP-DETR and DINO. We develop an uncertainty-guided logit modulation mechanism that leverages the uncertainty to modulate the class logits. Results corroborate the effectiveness of Cal-DETR against the competing train-time methods in calibrating both in-domain and out-domain detections.
  arXiv  Detail & Related papers  (2023-11-06T22:13:10Z)
• PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer [94.23904400441957]
  We introduce perturbation-based regularizers, which can smooth the loss landscape, into prompt tuning. We design two kinds of perturbation-based regularizers, including random-noise-based and adversarial-based. Our new algorithms improve the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks, respectively.
  arXiv  Detail & Related papers  (2023-05-03T20:30:51Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• Neural Networks with Quantization Constraints [111.42313650830248]
  We present a constrained learning approach to quantization training. We show that the resulting problem is strongly dual and does away with gradient estimations. We demonstrate that the proposed approach exhibits competitive performance in image classification tasks.
  arXiv  Detail & Related papers  (2022-10-27T17:12:48Z)
• SpeedLimit: Neural Architecture Search for Quantized Transformer Models [6.491305435530359]
  We introduce SpeedLimit, a novel Neural Architecture Search (NAS) technique that optimize accuracy whilst adhering to an upper-bound latency constraint. Our results underline the feasibility and efficacy of seeking an optimal balance between performance and latency, providing new avenues for deploying state-of-the-art transformer models in latency-sensitive environments.
  arXiv  Detail & Related papers  (2022-09-25T02:56:01Z)
• Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS [3.5786621294068373]
  We present LightWaveS, a framework for accurate multivariate time series classification. It employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent deep learning models. We show that we achieve speedup ranging from 9x to 65x compared to ROCKET during inference on an edge device.
  arXiv  Detail & Related papers  (2022-04-04T10:52:20Z)
• Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization [26.270754571140735]
  PIM accelerators often suffer from intrinsic noise in the physical components. We propose a noise-agnostic method to achieve robust neural network performance against any noise setting.
  arXiv  Detail & Related papers  (2020-07-07T06:51:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.