Ascend: A Scalable and Energy-Efficient Deep Neural Network Accelerator With Photonic Interconnects

Author:

Li Yuan1ORCID,Wang Ke1ORCID,Zheng Hao1ORCID,Louri Ahmed1,Karanth Avinash2ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, George Washington University, Washington, DC, USA

2. School of Electrical Engineering and Computer Science, Ohio University, Athens, OH, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A High-Performance and Energy-Efficient Photonic Architecture for Multi-DNN Acceleration;IEEE Transactions on Parallel and Distributed Systems;2024-01

2. A Silicon Photonic Multi-DNN Accelerator;2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT);2023-10-21

3. SEECHIP: A Scalable and Energy-Efficient Chiplet-based GPU Architecture Using Photonic Links;Proceedings of the 52nd International Conference on Parallel Processing;2023-08-07

4. Trade-Off-Oriented Impedance Optimization of Chiplet-Based 2.5-D Integrated Circuits With a Hybrid MDP Algorithm for Noise Elimination;IEEE Transactions on Circuits and Systems I: Regular Papers;2022-12

5. FSA: An Efficient Fault-tolerant Systolic Array-based DNN Accelerator Architecture;2022 IEEE 40th International Conference on Computer Design (ICCD);2022-10

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