Computation and memory optimized spectral domain convolutional neural network for throughput and energy-efficient inference

Rizvi, Shahriyar Masud; Rahman, Ab Al-Hadi Ab; Sheikh, Usman Ullah; Fuad, Kazi Ahmed Asif; Shehzad, Hafiz Muhammad Faisal

doi:10.1007/s10489-022-03756-1

Cited by 2 publications

(1 citation statement)

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“…It has been further reported that error tolerance decreases to 0.12 dB for OOK and 0.09 dB for PAM4 as the transmission distance is increased from 20 to 80 km. In contrast, in our work, we In this the Wide ResNet model is distinguished as the most effective model among LeNet and Inception-v4, showcasing similar computational complexities of approximately 0.613 billion Floating Point Operations (FLOPs) [42]. Its predominance is attributed to the minimal error tolerance it demonstrates, underscoring its tailored fit for the investigation.…”

Section: B Case 2: Pam4 Modulation Formatmentioning

confidence: 79%

Performance Evaluation of Optical Transmission Based on Link Estimation by Using Deep Learning Techniques

Al-Shamayleh,

Raza,

Ali

et al. 2024

IEEE Access

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In optical communication systems, the Q-factor is an important performance metric to evaluate the performance of an optical link. In this paper, a deep learning-based eye diagram analyzer is proposed to estimate the Q-factor. CNN architectures such as LeNet, Wide ResNet, and Inception-v4 are used for ON-Off Keying (OOK) and Pulse Amplitude Modulation (PAM) formats' eye diagrams. The performance of these architectures is evaluated in terms of accuracy, Mean Squared Error (MSE), and error tolerance. This work shows that Wide ResNet demonstrates better performance in both OOK and PAM4 transmission schemes, achieving MSE values of 0.00188 and 0.00036, respectively. Additionally, it attains a high R-squared (R2) value of 0.9998. This deep learning-based eye diagram analyzer may be a promising approach for analyzing and optimizing optical communication systems without extensive human intervention.

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Section: B Case 2: Pam4 Modulation Formatmentioning

confidence: 79%