基于随机计算的畴壁记忆深度卷积神经网络的面积和能量有效设计
An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
摘要：
随着可穿戴设备和物联网（IoT）的发展趋势，开发用于嵌入式应用的基于硬件的深度卷积神经网络（DCNN）变得很有吸引力，这需要低功耗/能耗和小硬件占用空间。最近的工作表明，随机计算（SC）技术可以从根本上简化算术单元的硬件实现，并有可能满足嵌入式设备中严格的功耗要求。然而，在这些工作中，存储器的设计优化被忽略的重量存储，这将不可避免地导致大的硬件成本。此外，如果传统的易失性SRAM或DRAM单元被用于权重存储，则每当DCNN平台被重新启动时，权重需要被重新初始化。为了克服这些限制，在这项工作中，我们采用了一种新兴的非易失性域壁存储器（DWM），它可以实现超高密度，以取代SRAM的重量存储在SC为基础的DCNN。我们提出了DW-CNN，这是第一个基于DWM的权重存储方法的综合设计优化框架。我们得出最佳的内存类型，精度和组织，以及是否存储二进制或随机数。我们提出了有效的资源共享方案，用于在基于SC的DCNN的卷积层和全连接层中进行基于DWM的权重存储，以在面积、功率（能量）消耗和应用级精度之间实现理想的平衡。
英文摘要：
With recent trend of wearable devices and Internet of Things (IoTs), it becomes attractive to develop hardware-based deep convolutional neural networks (DCNNs) for embedded applications, which require low power/energy consumptions and small hardware footprints. Recent works demonstrated that the Stochastic Computing (SC) technique can radically simplify the hardware implementation of arithmetic units and has the potential to satisfy the stringent power requirements in embedded devices. However, in these works, the memory design optimization is neglected for weight storage, which will inevitably result in large hardware cost. Moreover, if conventional volatile SRAM or DRAM cells are utilized for weight storage, the weights need to be re-initialized whenever the DCNN platform is re-started. In order to overcome these limitations, in this work we adopt an emerging non-volatile Domain-Wall Memory (DWM), which can achieve ultra-high density, to replace SRAM for weight storage in SC-based DCNNs. We propose DW-CNN, the first comprehensive design optimization framework of DWM-based weight storage method. We derive the optimal memory type, precision, and organization, as well as whether to store binary or stochastic numbers. We present effective resource sharing scheme for DWM-based weight storage in the convolutional and fully-connected layers of SC-based DCNNs to achieve a desirable balance among area, power (energy) consumption, and application-level accuracy.
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