Implementation and Optimization of LeNet-5 Model for Handwritten Digits Recognition on FPGAs using Brevitas and FINN

Njuguna, Josphat Chege; Çelebi, Aysun Taşyapı; Çelebi, Anıl

doi:10.1109/asyu58738.2023.10296630

Cited by 2 publications

(1 citation statement)

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“…They later implement the bitstream file into the PYNQ. Njuguna et al (2023) recently proposed the deployment of an optimised LeNet-5 model on Field Programmable Gate Arrays (FPGAs) using Brevitas and FINN frameworks for handwritten digit recognition. They reported that the network generalised well on a test set with an accuracy of 96.64% coupled with harnessing hardware resources of low power consumption for the deployment of the deep model.…”

Section: Lenet-5 and Its Impact On Computer Visionmentioning

confidence: 99%

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Ekundayo,

Ezugwu

2024

Preprint

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Deep learning stands at the forefront of contemporary machine learning techniques and is well-known for its outstanding predictive accuracy, adaptability to data variability, and remarkable ability to generalize across diverse domains. These attributes have spurred rapid progress and the emergence of novel iterations within the discipline. Yet, this swift evolution often obscures the foundational breakthroughs, with even trailblazing researchers at risk of fading into obscurity despite their seminal contributions. This study aims to provide a historical narrative of deep learning, tracing its origins from the cybernetic era to its current state-of-the-art status. We critically examine the contributions of individual pioneer scholars who have profoundly influenced the development of deep neural networks under the taxonomy of supervised, unsupervised, and reinforcement learning. Furthermore, the study also discusses the trending deep neural network architectures, explaining their operational principles, confronting associated challenges, exploring real-world applications, and outlining potential future trajectories that could offer a starting point for aspiring researchers in the field.

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Section: Lenet-5 and Its Impact On Computer Visionmentioning

confidence: 99%