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فصلنامه علمی کارافن

ارائه روشی برای کاهش اشکال همشنوایی در شبکه‌های روی تراشه سه‌بعدی با استفاده از شبکه‌های عصبی پیچشی

نوع مقاله : مقاله پژوهشی (نظری)

نویسندگان
1 دانشجوی کارشناسی مهندسی کامپیوتر، گروه مهندسی کامپیوتر، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران.
2 دانشیار ، گروه مهندسی کامپیوتر، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران.
چکیده
در این مقاله به بررسی چالش‌های طراحی و پیاده‌سازی مدارهای پردازشی پیشرفته در سیستم‌های چند هسته‌ای پرداخته شده است. روش‌های سنتی مقابله با همشنوایی شامل تکنیک‌های کدینگ و استفاده از تکرارکننده‌ها در سطح فیزیکی و ترانزیستوری است که با محدودیت‌هایی نظیر سربار افزایشی و پیچیدگی بالا مواجه‌اند. برای حل این مشکل، در این مقاله یک رویکرد نوین مبتنی بر هوش مصنوعی و یادگیری عمیق برای بهبود کارایی و کاهش مشکلات همشنوایی ارائه شده است. الگوریتم پیشنهادی با استفاده از الگوریتم‌های شبکه‌های عصبی پیچشی، قادر است با تحلیل و یادگیری الگوهای نامناسب و مضر در داده‌ها، اقدام به حذف آن‌ها و بهبود کیفیت سیگنال‌ها کند. نتایج شبیه‌سازی‌ها نشان می‌دهد که روش پیشنهادی می‌تواند الگوهای مضر را با دقت بالایی شناسایی و حذف کند و در نهایت منجر به افزایش سرعت پردازش و کاهش مصرف توان در تراشه‌ها شود. این رویکرد نه تنها عملکرد مدارهای پردازشی را بهبود می‌بخشد بلکه می‌تواند به عنوان یک راهکار مؤثر در طراحی نسل جدید تراشه‌های چند هسته‌ای مورد استفاده قرار گیرد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Presenting a method to reduce crosstalk in 3D on-chip networks using convolutional neural networks

نویسندگان English

Ali Fard 1
Zahra Shirmohammdi 2
1 Student of B.S in Computer Engineering, Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
2 Associate Professor, Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
چکیده English

In this paper, the challenges of designing and implementing advanced processing circuits in multi‌core systems have been investigated. Traditional methods of dealing with crosstalk include coding techniques and the use of physical and transistor repeaters, which face limitations such as increased overhead and high complexity. To solve this problem, in this paper, a new approach based on artificial intelligence and deep learning is presented to improve efficiency and reduce crosstalk problems. Using convolutional neural network algorithms, the proposed algorithm is capable of removing inappropriate and harmful patterns in the data and improving the quality of the signals by analyzing and learning them. The results of the simulations show that the proposed method can identify and remove harmful patterns with high accuracy and efficiency, and ultimately lead to an increase in processing speed and a decrease in power consumption in chips. This approach not only improves the performance of processing circuits, but can be used as an effective solution in designing the new generation of multi‌core chips.

کلیدواژه‌ها English

crosstalk
communication networks on a chip
multi-core systems
coding
deep learning
artificial intelligence
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دوره 22، شماره 1
فنی و مهندسی
بهار 1404
صفحه 105-126

  • تاریخ دریافت 07 آبان 1403
  • تاریخ بازنگری 25 اسفند 1403
  • تاریخ پذیرش 06 اردیبهشت 1404