Towards Efficient In-Memory Computing Hardware for Quantized Neural Networks: State-of-the-Art, Open Challenges and Perspectives

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 article 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.

Automated summary

The increasing amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and the growing concerns about data privacy are driving the transition from cloud-based to edge-based processing. Limited energy and computational resources on the edge are pushing the move from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques, such as quantization, are being applied to implement neural networks on limited hardware resources, reducing memory footprint, latency, and energy consumption. This article provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and connects software-based quantization approaches to IMC hardware implementation, while addressing open challenges, QNN design requirements, recommendations, and perspectives, along with an IMC-based QNN hardware roadmap.