بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ

ارکستراسیون داده ها در شتاب دهنده های یادگیری عمیق

Data Orchestration in Deep Learning Accelerators | Tushar Krishna, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, Ananda Samajdar | ISBN: 1681738694, 1681738716, 978-1681738697, 978-1681738710

30,000 تومان
محصول مورد نظر موجود نمی‌باشد.
تعداد
نوع
  • {{value}}
کمی صبر کنید...

کتاب های مرتبط

سال انتشار: 2020

تعداد صفحات: 166

زبان فایل: انگلیسی

فرمت فایل: pdf

حجم فایل: 13MB

ناشر: Morgan & Claypool

This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.