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.
| Autorius: | Nimish Shah, Marian Verhelst, Wannes Meert, |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2023 |
| Knygos puslapių skaičius: | 168 |
| ISBN-10: | 3031331354 |
| ISBN-13: | 9783031331350 |
| Formatas: | 241 x 160 x 15 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Efficient Execution of Irregular Dataflow Graphs: Hardware/Software Co-optimization for Probabilistic AI and Sparse Linear Algebra“
