二值化深度神经网络的内存和错误免疫差分RRAM实现
In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks
摘要：
基于RRAM存内计算是实现高能效神经网络的一条令人兴奋的道路。然而，这种设想受到RRAM可变性的挑战，因为存内计算的有效实现不允许纠错。在这项工作中，我们制作和测试了差分HfO2为基础的存储器结构和相关的检测电路，这是理想的存内计算。这是第一次，我们表明，我们的方法实现了相同的可靠性的好处，纠错，但没有任何CMOS开销。我们还首次证明，它可以自然地实现二值化深度神经网络，这是人工智能的最新发展，具有极高的能效，并且该系统完全符合图像识别应用。最后，我们证明了差分存储器提供的额外可靠性如何允许在低电压条件下对器件进行编程，这些器件具有数十亿次循环的高耐久性。
英文摘要：
RRAM-based in-Memory Computing is an exciting road for implementing highly energy efficient neural networks. This vision is however challenged by RRAM variability, as the efficient implementation of in-memory computing does not allow error correction. In this work, we fabricated and tested a differential HfO2-based memory structure and its associated sense circuitry, which are ideal for in-memory computing. For the first time, we show that our approach achieves the same reliability benefits as error correction, but without any CMOS overhead. We show, also for the first time, that it can naturally implement Binarized Deep Neural Networks, a very recent development of Artificial Intelligence, with extreme energy efficiency, and that the system is fully satisfactory for image recognition applications. Finally, we evidence how the extra reliability provided by the differential memory allows programming the devices in low voltage conditions, where they feature high endurance of billions of cycles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
