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Gibbon: An Efficient Co-Exploration Framework of NN model and Processing-In-Memory Architecture

Sun, Hanbo ; Zhu, Zhenhua ; Wang, Chenyu ; Ning, Xuefei ; Dai, Guohao ; Yang, Huazhong ; Wang, Yu

IEEE transactions on computer-aided design of integrated circuits and systems, 2023-11, Vol.42 (11), p.1-1 [Periódico revisado por pares]

New York: IEEE

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  • Título:
    Gibbon: An Efficient Co-Exploration Framework of NN model and Processing-In-Memory Architecture
  • Autor: Sun, Hanbo ; Zhu, Zhenhua ; Wang, Chenyu ; Ning, Xuefei ; Dai, Guohao ; Yang, Huazhong ; Wang, Yu
  • Assuntos: Adaptive algorithms ; Algorithms ; Artificial neural networks ; Co-design ; Computer architecture ; Computer memory ; Computer simulation ; Evolutionary algorithms ; Flight simulators ; Hardware ; Hardware and Software Co-exploration ; Mathematical models ; Neural Architecture Search ; Neural Network Accelerator ; Optimization ; Parameters ; Processing-In-Memory Architectures ; Search algorithms
  • É parte de: IEEE transactions on computer-aided design of integrated circuits and systems, 2023-11, Vol.42 (11), p.1-1
  • Descrição: The memristor-based Processing-In-Memory (PIM) architectures have been proven to be a potential architecture to store enormous parameters and execute the complicated computations of Deep Neural Networks (DNNs) efficiently. Existing PIM studies focus on designing high energy-efficient hardware architecture and algorithm-hardware co-optimization for better performance. However, the impacts of the algorithms and hardware architectures on the performance intersect with each other. Only optimizing the algorithms or the hardware architectures can not realize the optimal design. Therefore, the co-exploration of NN models and PIM architecture is necessary. However, for one thing, the co-exploration space size of NN models and PIM architectures is extremely huge, and is challenging to search. For another, during the co-exploration process, time-consuming PIM simulators are needed to evaluate various design candidates and pose a heavy time burden. To tackle these problems, we propose an efficient co-exploration framework of NN models and PIM architectures, named . In, the co-exploration space is carefully designed to adapt both NN models and PIM architectures. Besides, in order to improve search efficiency, we propose an evolutionary search algorithm with adaptive parameter priority (ESAPP). In addition, introduces a multi-level joint simulator to alleviate the problem of time-consuming evaluation. The experimental results show that the proposed co-exploration framework can find better NN models and PIM architectures than existing studies in only six GPU hours (9.8 48.2× speedup). At the same time, can improve the accuracy of co-design results by 15.3% and reduce the energy-delay-product (EDP) by 5.96× compared with existing work.
  • Editor: New York: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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