Data Orchestration in Deep Learning Accelerators

By: Ananda Samajdar, Tushar Krishna, Michael Pellauer, Hyoukjun Kwon, Angshuman Parashar

Write A Review

Paperback | 18 August 2020

At a Glance

Paperback
168 Pages

Dimensions(cm)
23.5 x 19.08 x 0.91

Paperback

$89.99

or 4 interest-free payments of $22.50 with

Ships in 5 to 7 business days

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.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $0.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.