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Efficient Execution of Irregular Dataflow Graphs

Hardware/Software Co-optimization for Probabilistic AI and Sparse Linear Algebra

Shah, Nimish   Verhelst, Marian   Meert, Wannes
Englisch | Hardcover

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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Produktinformationen "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.

Untertitel
Hardware/Software Co-optimization for Probabilistic AI and Sparse Linear Algebra
H | B | T | Gramm
241 mm | 160 mm | 15 mm | 424 gr
Erscheinungsjahr
2023
FSK
0
Ausgabe
Hardcover
Verlag
Springer
ISBN-10
3031331354
ISBN-13
9783031331350
Autor
Verhelst, Marian; Meert, Wannes; Shah, Nimish
Sprache
Englisch
Seitenanzahl
168
Themen
Maschinelles Lernen, Schaltkreise und Komponenten Bauteile, Maschinelles Lernen, Eingebettete Systeme
Verantwortliche Person gemäß Art. 16 GPSR
Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115, Heidelberg, DE, ProductSafety@springernature.com

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Efficient Execution of Irregular Dataflow Graphs
Efficient Execution of Irregular Dataflow Graphs
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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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Efficient Execution of Irregular Dataflow Graphs
Efficient Execution of Irregular Dataflow Graphs
Shah, Nimish
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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