SAL: Optimizing the Dataflow of Spin-based Architectures for Lightweight Neural Networks

Abstract

As the Convolutional Neural Network (CNN) goes deeper and more complex, the network becomes memory-intensive and computation-intensive. To address this issue, the lightweight neural network reduces parameters and Multiplication-and-Accumulation (MAC) operations by using the Depthwise Separable Convolution (DSC) to improve speed and efficiency. Nonetheless, the energy efficiency of classical Von Neumann architectures for CNNs is limited due to the memory wall challenge. Spin-based architectures have the potential to address this challenge thanks to the integration of memory and computing with ultra-high energy efficiency. However, deploying the DSC on spin-based architectures with the traditional dataflow leads to huge activation movements and low hardware utilization. Moreover, the inter-layer data dependency of neural networks increases latency. These factors become the bottleneck of improving energy efficiency and performance. Inspired by these challenges, we propose a novel dataflow on Spin-based Architectures for Lightweight neural networks (SAL). The novel dataflow replaces convolution unrolling by selecting activations in the crossbar according to the convolution window and also realizes the inter-layer data reuse. Moreover, the novel dataflow also reduces the latency due to the data dependency between layers, realizing higher performance. To the best of our knowledge, this is the first design to use hybrid dataflow for the PIM architecture. We also optimize the structure of the spin-based crossbar and the pipeline based on the dataflow to achieve better data reuse and computational parallelism. For deploying the MobileNet V1, the novel dataflow improves the hardware utilization by 23×∼ 105× and reduces the data traffic by 1.09×∼ 18.6×. Compared with the NEBULA, a spin-based non-Von Neumann architecture, the SAL reduces the energy consumption by 4× and improves the performance by 7.3×, which are 0.32 mJ and 10.43 GOPs -1 , respectively. Moreover, the SAL improves power efficiency over 29 times more than the NEBULA. Compared with the Eyeriss, the SAL improves the energy efficiency by four orders of magnitude.