论文标题
朝着有效的基于RRAM的量化神经网络硬件：最新和开放问题
Towards Efficient RRAM-based Quantized Neural Networks Hardware: State-of-the-art and Open Issues
论文作者
论文摘要
在边缘处理的数据量增加以及减少大型神经网络体系结构能源消耗的需求已引发了从传统的von Neumann架构向内存计算范式的过渡。量化是通过限制位精度来降低神经网络功率和计算要求的方法之一。电阻随机访问记忆（RRAM）设备是量化神经网络（QNN）实现的绝佳候选者。由于RRAM中可能的导电状态数量有限，因此在设计基于RRAM的神经网络时始终考虑一定程度的量化。在这项工作中，我们对基于RRAM的QNN实现进行了全面分析，这表明RRAM在满足有效QNN硬件的标准方面的位置。我们涵盖了与QNN相关的硬件和设备挑战，并展示了未解决的主要问题以及可能的未来研究方向。
The increasing amount of data processed on edge and the demand for reducing the energy consumption for large neural network architectures have initiated the transition from traditional von Neumann architectures towards in-memory computing paradigms. Quantization is one of the methods to reduce power and computation requirements for neural networks by limiting bit precision. Resistive Random Access Memory (RRAM) devices are great candidates for Quantized Neural Networks (QNN) implementations. As the number of possible conductive states in RRAMs is limited, a certain level of quantization is always considered when designing RRAM-based neural networks. In this work, we provide a comprehensive analysis of state-of-the-art RRAM-based QNN implementations, showing where RRAMs stand in terms of satisfying the criteria of efficient QNN hardware. We cover hardware and device challenges related to QNNs and show the main unsolved issues and possible future research directions.
