Titlebook: Efficient Execution of Irregular Dataflow Graphs; Hardware/Software Co Nimish Shah,Wannes Meert,Marian Verhelst Book 2023 The Editor(s) (if

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Slit-Lamp

Suitable Data Representation: A Study of Fixed-Point, Floating-Point, and PositTM Formats for Probanvestigates whether probabilistic AI models and sparse linear algebra workloads lend themselves to this optimization. To systematically answer this, analytical error and energy models are developed by exploiting the properties of the workloads. A framework called . is designed that uses these models

Pde5-Inhibitors

: Constrained-Optimization- Based Parallelization of Irregular Workloads for Multicore Processors,CPUs is challenging due to the irregularity of the underlying computational dataflow graphs. For efficient execution of these graphs, high workload balance and minimal communication and synchronization overheads need to be achieved, in the presence of dataflow graph’s irregularity..To this end, this

揭穿真相

DAG Processing Unit Version 1 (DPU): Efficient Execution of Irregular Workloads on a Multicore ProcCPUs and GPUs despite highly optimized software parallelization, resulting in severe underutilization of the hardware. This chapter discusses the first version of DAG processing unit (DPU), a specialized processor that addresses the limitations of existing hardware, enabling efficient execution of i

提煉

指耕作

g set of AI workloads that rely on sparse matrix operations .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

指耕作

Terrace

https://doi.org/10.1007/978-3-662-00283-4optimal architecture configuration that minimizes the energy-delay product. This hardware-software co-optimization approach results in a speedup of 1.4×, 3.5×, and 14× over the first version of DPU, a CPU, and a GPU, respectively, while also achieving a lower energy-delay product.

PET-scan

DAG Processing Unit Version 2 (DPU-v2): Efficient Execution of Irregular Workloads on a Spatial Datoptimal architecture configuration that minimizes the energy-delay product. This hardware-software co-optimization approach results in a speedup of 1.4×, 3.5×, and 14× over the first version of DPU, a CPU, and a GPU, respectively, while also achieving a lower energy-delay product.

BLAZE

,Besuch im künstlichen Paradies,mization solvers, . combines the solver with several heuristics that can handle dataflow graphs with millions of nodes without sacrificing the quality of parallelization. Experiments on a multicore CPU show a speedup of 2.0× over existing libraries.