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A Heterogeneous System With Computing in Memory Processing Elements to Accelerate CNN Inference
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DOI：10.1109/TCSI.2025.3543104.png)
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Abstract
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Computing in memory (CIM) is one of the promising solutions to improve computing performance by integrating logic in memory. This work presents an efficient heterogeneous system based on the ultrafast CIM architecture (HS-CIM) to accelerate convolutional neural network (CNN) inference. First, an ultrafast CIM architecture is proposed based on the static random access memory (SRAM) by utilizing the novel total input and full digital scheme to implement multiply-and-accumulate (MAC) operation, which effectively addresses the high delay issue caused by high-precision computing in CIM architecture. Second, a heterogeneous system based on the proposed CIM architecture (HS-CIM) has been constructed with an aligned global cache and an adaptive pruning scheme to eliminate performance degradation and accuracy loss caused by input data bandwidth limitations. Meanwhile, efficient input data and weight data mapping schemes are proposed to minimize the delay and energy caused by input data transmission from the cache to the CIM architecture, thus realizing efficient CNN inference in the HS-CIM system. Finally, we analyze the performance of HS-CIM at the layout level by implementing the LeNet-5 and VGG models of CNN to recognize the image of MNIST and CIFAR-10 datasets, respectively. Results show that the energy efficiency of the proposed CIM architecture achieves 58.32 TOPS/W with 8-bit input/weight precision. Meanwhile, the inference accuracy of the HS-CIM system for MNIST and CIFAR-10 is 99.25% and 92.34%, respectively.
Keywords:
Computing in memory (CIM)
heterogeneous system
SRAM
CNN
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5.2
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