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

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 cannot 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 Gibbon. In Gibbon, 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, Gibbon introduces a multilevel 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\times $ – $48.2\times $ speed-up). At the same time, Gibbon can improve the accuracy of co-design results by 15.3% and reduce the energy–delay-product (EDP) by $5.96\times $ compared with existing work.