ارائه تمام‌جمع‌کننده‌های تقریبی با خطای کم و آگاه از تغییرپذیری برای کاربردهای تحمل پذیر عدم دقت
محورهای موضوعی : مهندسی برق و کامپیوتر
محمد میرزایی
1
(دانشگاه تهران،دانشکده مهندسی برق و کامپیوتر)
سيامك محمدي
2
(دانشگاه تهران،دانشکده مهندسی برق و کامپیوتر)
کلید واژه: تغییرپذیری, تمام‌جمع‌کننده تقریبی, جمع‌کننده تقریبی, کاربردهای تحمل‌پذیر عدم دقت, محاسبات تقریبی,
چکیده مقاله :
کاربردهای تحمل‌پذیر عدم دقت مانند پردازش تصویر و یادگیری ماشین به دلیل محدودیت‌های حس انسان یا ماهیت کاربرد، قابلیت تحمل عدم دقت را دارند. استفاده از محاسبات تقریبی در این کاربردها می‌تواند به کاهش قابل توجهی در توان، تأخیر و مساحت منجر شود. در این مقاله دو تمام‌جمع‌کننده تقریبی و یک جمع‌کننده تقریبی با خطای کم ارائه شده و اثرات تغییرپذیری قالب به قالب ولتاژ آستانه روی این مدارها مورد ارزیابی قرار گرفته است. برای ارزیابی خطا و تغییرپذیری، از این تمام‌جمع‌کننده‌های تقریبی در ساختار جمع‌کننده با انتشار نقلی و الگوریتم‌های پردازش تصویر sharpening و smoothing استفاده شده است. از نظر سه پارامتر حاصل‌ضرب- توان- تأخیر، دقت و مساحت برای ورودی‌های با توزیع یکنواخت، تمام‌جمع‌کننده پیشنهادی 1 و از نظر حداکثر نسبت سیگنال به نویز برای کاربردهای واقعی، تمام‌جمع‌کننده پیشنهادی 2 و جمع‌کننده پیشنهادی، بهترین عملکرد را دارند.
In imprecision-tolerant applications such as image processing and machine learning, imprecision can be tolerated because of the nature of the application itself or the limitation of human senses. By using the approximate computation in these applications, significant power, delay, or area reductions can be achieved. In this paper, two approximate full adders and an approximate adder, with low error are proposed. The effects of die-to-die (D2D) process variation on the threshold voltage of approximate circuits have been evaluated. For evaluating the accuracy and the variability, these approximate full adders have been used and analyzed in the ripple carry adder structure, image Sharpening and image Smoothing algorithms. In terms of power-delay-product (PDP), accuracy, and area for uniformly distributed inputs, the proposed approximate full adder 1, exhibits the best performance, and the proposed approximate full adder 2 and the proposed approximate adder, show the best peak-signal-to-noise ratio (PSNR) for real images.
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