標(biāo)題: Titlebook: Efficient Execution of Irregular Dataflow Graphs; Hardware/Software Co Nimish Shah,Wannes Meert,Marian Verhelst Book 2023 The Editor(s) (if [打印本頁(yè)] 作者: 吞食 時(shí)間: 2025-3-21 19:43
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書(shū)目名稱(chēng)Efficient Execution of Irregular Dataflow Graphs讀者反饋
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作者: Slit-Lamp 時(shí)間: 2025-3-21 21:19
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作者: Pde5-Inhibitors 時(shí)間: 2025-3-22 02:44
: 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作者: 揭穿真相 時(shí)間: 2025-3-22 07:06
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作者: 提煉 時(shí)間: 2025-3-22 11:51 作者: 指耕作 時(shí)間: 2025-3-22 13:20
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作者: 指耕作 時(shí)間: 2025-3-22 17:59 作者: Terrace 時(shí)間: 2025-3-22 21:14
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.作者: PET-scan 時(shí)間: 2025-3-23 05:08
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.作者: BLAZE 時(shí)間: 2025-3-23 07:18
,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.作者: 起波瀾 時(shí)間: 2025-3-23 11:40 作者: Intend 時(shí)間: 2025-3-23 14:15
Suitable Data Representation: A Study of Fixed-Point, Floating-Point, and PositTM Formats for Proba and generates low-precision fully spatial pipelined hardware, achieving up to 67% energy reduction compared to 32b floating point. Based on the findings of ., the suitability of a novel data representation called posit. is investigated.作者: IOTA 時(shí)間: 2025-3-23 20:09
DAG Processing Unit Version 1 (DPU): Efficient Execution of Irregular Workloads on a Multicore Procrregular dataflow graphs. DPU is equipped with parallel compute units that execute different subgraphs of a dataflow graph independently, yet, can synchronize within a clock cycle using a hardware-supported synchronization primitive, and communicate efficiently via a flexible interconnect to a global banked scratchpad.作者: 終止 時(shí)間: 2025-3-24 01:34
Book 2023ntire 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..作者: Palatial 時(shí)間: 2025-3-24 02:29 作者: Mirage 時(shí)間: 2025-3-24 08:45 作者: 外貌 時(shí)間: 2025-3-24 11:01
https://doi.org/10.1007/978-3-0348-6443-5 and generates low-precision fully spatial pipelined hardware, achieving up to 67% energy reduction compared to 32b floating point. Based on the findings of ., the suitability of a novel data representation called posit. is investigated.作者: 惰性女人 時(shí)間: 2025-3-24 15:58
,Von den Verh?ltnissen und Proportionen,rregular dataflow graphs. DPU is equipped with parallel compute units that execute different subgraphs of a dataflow graph independently, yet, can synchronize within a clock cycle using a hardware-supported synchronization primitive, and communicate efficiently via a flexible interconnect to a global banked scratchpad.作者: 山頂可休息 時(shí)間: 2025-3-24 22:15 作者: Insubordinate 時(shí)間: 2025-3-25 01:08
Nimish Shah,Wannes Meert,Marian VerhelstAnalyzes the key bottlenecks in the existing platforms for these sparse and irregular AI and linear algebra algorithms;.Discusses an emerging set of AI workloads that rely on sparse matrix operations 作者: dearth 時(shí)間: 2025-3-25 06:21
http://image.papertrans.cn/e/image/302977.jpg作者: 非實(shí)體 時(shí)間: 2025-3-25 09:09 作者: 強(qiáng)制性 時(shí)間: 2025-3-25 13:28 作者: 輕而薄 時(shí)間: 2025-3-25 18:10
,Einflussgr??en des Bewertungsunterschieds,is book from the domains of probabilistic AI and sparse linear algebra, which are at risk of losing the hardware lottery, are introduced. Subsequently, four open research questions are discussed that, if answered, would help win the hardware lottery for these workloads. Finally, the contributions of作者: FLIRT 時(shí)間: 2025-3-25 23:41 作者: 嚴(yán)厲批評(píng) 時(shí)間: 2025-3-26 03:11 作者: Fibroid 時(shí)間: 2025-3-26 04:27 作者: 流浪 時(shí)間: 2025-3-26 09:17
https://doi.org/10.1007/978-3-662-00283-4s of probabilistic AI and sparse linear algebra. It consists of a tree-structured datapath for efficient data reuse, a customized banked register file, and targeted interconnects tuned to support irregular register accesses. DPU-v2 is utilized effectively through a custom compiler that systematicall作者: Magnitude 時(shí)間: 2025-3-26 14:34 作者: DRAFT 時(shí)間: 2025-3-26 19:33 作者: 掃興 時(shí)間: 2025-3-27 00:47
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