Convolutional Neural Networks using FPGA-based Pipelining
نویسندگان
چکیده
In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of the use FPGA-based pipelining for hardware acceleration CNNs. These days, most people (CNNs) perform computer vision tasks like picture categorization and object recognition. The processing memory demands CNNs, however, can be excessive, especially real-time applications. has emerged as viable option thanks its parallel capabilities low power consumption. examination describes fundamentals basic structure (CNNs). current best practises developing pipelined accelerators CNNs on FPGAs are then reviewed, covering topics partitioning pipelining. Area limits, needs, latency considerations only some difficulties trade-offs discussed in article. addition, survey evaluates contrasts various FPGA terms performance, energy consumption, resource utilisation. Future directions potential research areas also paper, such approximate computing techniques, integration reconfigurable architectures with emerging technologies, exploration hybrid that combine other accelerators. This was created aid researchers practitioners efficient effective by providing thorough trends issues
منابع مشابه
Design Space Exploration of FPGA-Based Deep Convolutional Neural Networks
Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance and energy-efficiency constraints. Leverages all sources of parallelism in DCNNs, in this paper w...
متن کاملCystoscopy Image Classication Using Deep Convolutional Neural Networks
In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...
متن کاملrodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
EMG-based wrist gesture recognition using a convolutional neural network
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Iraqi journal for computer science and mathematics
سال: 2023
ISSN: ['2788-7421']
DOI: https://doi.org/10.52866/ijcsm.2023.02.02.019