The amount of data processed in the cloud, the development of
Internet-of-Things (IoT) applications, and growing data privacy concerns force
the transition from cloud-based to edge-based processing. Limited energy and
computational resources on edge push the transition from traditional von
Neumann architectures to In-memory Computing (IMC), especially for machine
learning and neural network applications. Network compression techniques are
applied to implement a neural network on limited hardware resources.
Quantization is one of the most efficient network compression techniques
allowing to reduce the memory footprint, latency, and energy consumption. This
paper provides a comprehensive review of IMC-based Quantized Neural Networks
(QNN) and links software-based quantization approaches to IMC hardware
implementation. Moreover, open challenges, QNN design requirements,
recommendations, and perspectives along with an IMC-based QNN hardware roadmap
are provided.

云处理、边缘处理、内存计算、网络压缩技术、量化神经网络是本研究的主要关键词。本文提供了内存计算量化神经网络的全面回顾，并将软件量化方法与内存计算硬件实现相结合。此外，还提供了挑战、设计要求、建议以及基于内存计算量化神经网络的硬件发展路线图。