Data Orchestration in Deep Learning Accelerators
|Morgan & Claypool |
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|Tushar Krishna, Georgia Institute of Technology|
Hyoukjun Kwon, Georgia Institute of Technology
Angshuman Parashar, NVIDIA
Michael Pellauer, NVIDIA
Ananda Samajdar, Georgia Institute of Technology
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
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 engineers,
researchers, and students interested in designing high-performance and
low-energy accelerators for DNN inference.