تشخیص و کاهش خرابی ساکت داده براساس پیش بینی نرخ رخداد خرابی بدون تزریق اشکال

الموضوعات : سامانه‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند

مونا یخچی ¹ (دانشجوی دکتری، گروه کامپیوتر، واحد بروجرد، دانشگاه آزاد اسلامی، بروجرد، ایران.)
مهدی فاضلی ² (دانشیار، گروه کامپیوتر ، دانشکده فناوری اطلاعات، دانشگاه هالمستاد، هالمستاد، سوند.)
سید امیر اصغری توچائی ³ (استادیار، گروه کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی خوارزمی، تهران، ایران.)

تاريخ الإرسال : 27 الجمعة , صفر, 1444 تاريخ التأكيد : 18 الجمعة , شعبان, 1444 تاريخ الإصدار : 29 الجمعة , جمادى الأولى, 1444

الکلمات المفتاحية: تزریق اشکال, خطاهای چند بیتی, خطاهای نرم, یادگیری ماشین, خرابی ساکت داده,

ملخص المقالة :

خرابی ساکت داده (SDC) به طور جدی قابلیت اطمینان یک سیستم را به مخاطره می‌اندازد. رویکردهای فعلی با استفاده از یادگیری ماشین نرخ رخداد SDC برای هر دستورالعمل‌ را پیش بینی می‌کنند. در حالی‌که اکثر آنها فاقد دقت مناسب و نیازمند مجموعه داده برای آموزش هستند و به دلیل مصرف منابع زیاد دستیابی به آنها دشوار است. از سوی دیگر نرخ رخداد اشکالات چندبیتی در قطعات نیمه هادی افزایش چشمگیری داشتهاند. لذا تشخیص دستورات آسیب پذیر در حضور اشکال اهمیت یافته است. اما خلاء تحقیقات موجود عدم وجود یک روش نرم افزاری با دقت بالا بدون نیاز به تزریق اشکال است؛ به طوریکه تشخیص اشکال در SDC با منشاء داده و دستورالعمل مورد بررسی قرار بگیرد. بدین منظور، در این پژوهش با محاسبه نرخ رخداد SDC برای هر دستورالعمل‌ها، مدل درخت تصمیم گیری M5rule پیشنهاد گردیده است. سپس از روش تشخیص خطا، با کپی کردن دستورالعملهای حیاتی بوسیله مرتبسازی استفاده شده و در نهایت مدل ارائه شده بر روی معیار Mibench با برنامه‌های آزمایشی متعدد ارزیابی گردیده است. نتایج ارزیابی نشان می‌دهد روش ارائه شده در مقایسه با سایر روش‌های پیشرفته به دقت تشخیص بهتری با سربار در حدود 99 درصد برای 58 درصد نرخ پوشش SDC رسیده است.

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