On-chip Interconnect Trade-offs for Tera-scale Many-core Processors

2010 ◽  
pp. 263-296 ◽  
Author(s):  
Mani Azimi ◽  
Donglai Dai ◽  
Akhilesh Kumar ◽  
Aniruddha Vaidya
Keyword(s):  
Trade Offs ◽  
On Chip ◽  
Many Core

scholarly journals A Survey of Software-Defined Networks-on-Chip: Motivations, Challenges and Opportunities

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 183
Author(s):  
Jose Ricardo Gomez-Rodriguez ◽  
Remberto Sandoval-Arechiga ◽  
Salvador Ibarra-Delgado ◽  
Viktor Ivan Rodriguez-Abdala ◽  
Jose Luis Vazquez-Avila ◽  
...  
Keyword(s):  
Single Chip ◽  
Synthesis Time ◽  
Networks On Chip ◽  
Data Dependencies ◽  
Layered Architecture ◽  
Systems On Chip ◽  
Challenges And Opportunities ◽  
Computing Platforms ◽  
On Chip ◽  
Many Core

Current computing platforms encourage the integration of thousands of processing cores, and their interconnections, into a single chip. Mobile smartphones, IoT, embedded devices, desktops, and data centers use Many-Core Systems-on-Chip (SoCs) to exploit their compute power and parallelism to meet the dynamic workload requirements. Networks-on-Chip (NoCs) lead to scalable connectivity for diverse applications with distinct traffic patterns and data dependencies. However, when the system executes various applications in traditional NoCs—optimized and fixed at synthesis time—the interconnection nonconformity with the different applications’ requirements generates limitations in the performance. In the literature, NoC designs embraced the Software-Defined Networking (SDN) strategy to evolve into an adaptable interconnection solution for future chips. However, the works surveyed implement a partial Software-Defined Network-on-Chip (SDNoC) approach, leaving aside the SDN layered architecture that brings interoperability in conventional networking. This paper explores the SDNoC literature and classifies it regarding the desired SDN features that each work presents. Then, we described the challenges and opportunities detected from the literature survey. Moreover, we explain the motivation for an SDNoC approach, and we expose both SDN and SDNoC concepts and architectures. We observe that works in the literature employed an uncomplete layered SDNoC approach. This fact creates various fertile areas in the SDNoC architecture where researchers may contribute to Many-Core SoCs designs.

scholarly journals Near-Optimal Thermal Monitoring Framework for Many-Core Systems-on-Chip

2015 ◽  
Vol 64 (11) ◽  
pp. 3197-3209 ◽  
Author(s):  
Juri Ranieri ◽  
Alessandro Vincenzi ◽  
Amina Chebira ◽  
David Atienza ◽  
Martin Vetterli
Keyword(s):  
Thermal Monitoring ◽  
Systems On Chip ◽  
Monitoring Framework ◽  
On Chip ◽  
Many Core

MCVP-NoC: Many-Core Virtual Platform with Networks-on-Chip support

2013 ◽  
Author(s):  
Dexue Zhang ◽  
Xiaoyang Zeng ◽  
Zongyan Wang ◽  
Weike Wang ◽  
Xinhua Chen
Keyword(s):  
Networks On Chip ◽  
Virtual Platform ◽  
On Chip ◽  
Many Core

FoToNoC: A Folded Torus-Like Network-on-Chip Based Many-Core Systems-on-Chip in the Dark Silicon Era

2017 ◽  
Vol 28 (7) ◽  
pp. 1905-1918 ◽  
Author(s):  
Lei Yang ◽  
Weichen Liu ◽  
Weiwen Jiang ◽  
Mengquan Li ◽  
Peng Chen ◽  
...  
Keyword(s):  
Network On Chip ◽  
Dark Silicon ◽  
Systems On Chip ◽  
On Chip ◽  
Many Core

scholarly journals Compiler-directed scratchpad memory data transfer optimization for multithreaded applications on a heterogeneous many-core architecture

2021 ◽  
Author(s):  
Xiaohan Tao ◽  
Jianmin Pang ◽  
Jinlong Xu ◽  
Yu Zhu
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Scientific Computing ◽  
Data Transfer ◽  
Performance Model ◽  
Experimental Result ◽  
Transfer Model ◽  
Scratchpad Memory ◽  
On Chip ◽  
Many Core

AbstractThe heterogeneous many-core architecture plays an important role in the fields of high-performance computing and scientific computing. It uses accelerator cores with on-chip memories to improve performance and reduce energy consumption. Scratchpad memory (SPM) is a kind of fast on-chip memory with lower energy consumption compared with a hardware cache. However, data transfer between SPM and off-chip memory can be managed only by a programmer or compiler. In this paper, we propose a compiler-directed multithreaded SPM data transfer model (MSDTM) to optimize the process of data transfer in a heterogeneous many-core architecture. We use compile-time analysis to classify data accesses, check dependences and determine the allocation of data transfer operations. We further present the data transfer performance model to derive the optimal granularity of data transfer and select the most profitable data transfer strategy. We implement the proposed MSDTM on the GCC complier and evaluate it on Sunway TaihuLight with selected test cases from benchmarks and scientific computing applications. The experimental result shows that the proposed MSDTM improves the application execution time by 5.49$$\times$$ × and achieves an energy saving of 5.16$$\times$$ × on average.

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