Resistive Random access memory (RRAM) devices together with the material implication (IMPLY) logic are a promising computing scheme for realizing energy efficient reconfigurable computing hardware for edge computing applications. This approach has been recently shown to enable the in-memory implementation of Binarized Neural Networks. However, an accurate analysis of the performance achieved on a real classification task are still missing. In this work, we train and estimate the performance of an IMPLY-based implementation of a multilayer perceptron (MLP) BNN and highlight its main reliability challenges by using a physics-based RRAM compact model calibrated on three RRAM technologies from the literature. We then show how the smart IMPLY (SIMPLY) architecture solves the reliability issues of conventional IMPLY architectures and compare its performance with respect to conventional solutions considering different parallelization degree. The worst-case energy estimates for an inference task performed on the trained network, show that the SIMPLY implementation results in a >46 energy-delay-product (EDP) improvement with respect to a conventional low-power embedded system implementation.

中文翻译：

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基于内存逻辑的二值化神经网络的可靠性和性能分析

电阻式随机存取存储器 (RRAM) 设备与材料蕴涵 (IMPLY) 逻辑一起是一种有前途的计算方案，可用于为边缘计算应用实现节能的可重构计算硬件。这种方法最近已被证明可以在内存中实现二值化神经网络。但是，仍然缺少对在实际分类任务上实现的性能的准确分析。在这项工作中，我们训练和估计基于 IMPLY 的多层感知器 (MLP) BNN 实现的性能，并通过使用基于物理的 RRAM 紧凑模型来强调其主要的可靠性挑战，该模型在文献中的三种 RRAM 技术上校准。然后，我们展示了智能 IMPLY (SIMPLY) 架构如何解决传统 IMPLY 架构的可靠性问题，并将其性能与考虑不同并行度的传统解决方案进行比较。在经过训练的网络上执行的推理任务的最坏情况能量估计表明，相对于传统的低功耗嵌入式系统实现，SIMPLY 实现导致了 >46 能量延迟积 (EDP) 的改进。