Data Orchestration in Deep Learning Accelerators
Data Orchestration in Deep Learning Accelerators This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. Modern DNNs have millions of hyper parameters…
Specifikacia Data Orchestration in Deep Learning Accelerators
Data Orchestration in Deep Learning Accelerators
This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. 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.
The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It 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.
Deep Learning in Science
Learning Deep Learning
Deep Learning
Deep Learning
Orchestration
Generative Deep Learning
Generative Deep Learning
Probabilistic Deep Learning