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Efficient Execution of Irregular Dataflow Graphs

Bag om Efficient Execution of Irregular Dataflow Graphs

This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031331350
  • Indbinding:
  • Hardback
  • Sideantal:
  • 168
  • Udgivet:
  • 14. juli 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 160x15x241 mm.
  • Vægt:
  • 424 g.
  • BLACK NOVEMBER
  Gratis fragt
Leveringstid: 8-11 hverdage
Forventet levering: 7. december 2024

Beskrivelse af Efficient Execution of Irregular Dataflow Graphs

This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.

Brugerbedømmelser af Efficient Execution of Irregular Dataflow Graphs



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