Resistive random access memory (RRAM) is a promising technology for energy-efficient
neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model …

Amidst the unfolding technological revolution and industrial transformation, the synergistic
development between China's automotive and semiconductor industries has emerged as a …

Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory
and interconnection cost on the hardware platform. Existing pruning approaches remove …

Artificial intelligence (AI) systems have made significant impact on the society in the recent
years in a wide range of fields, including healthcare, transportation, and finances. In …

There is an increasing need of continual learning in dynamic systems, such as the self-
driving vehicle, the surveillance drone, and the robotic system. Such a system requires …

In-memory computing (IMC) provides a dense and parallel structure for high performance
and energy-efficient acceleration of deep neural networks (DNNs). The increased …

Abstract In-memory computing (IMC)-based hardware reduces latency as well as energy
consumption for compute-intensive machine learning (ML) applications. Till date, several …

There is an increasing need of continual learning in dynamic systems, such as the self-
driving vehicle, the surveillance drone, and the robotic system. Such a system requires …

Abstract In-memory computing (IMC)-based hardware reduces latency and energy
consumption for compute-intensive machine learning (ML) applications. Several …

This paper reports the main State-Of-The-Art algorithmic enablers for compact Neural
Network topology design, while relying on basic numerical experiments. Embedding …