The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

DNN accelerators are often developed and evaluated in isolation without considering the
cross-stack, system-level effects in real-world environments. This makes it difficult to …

This paper presents a detailed overview of sparsity exploitation in deep neural network
(DNN) accelerators. Despite the algorithmic advancements which drove DNNs to become …

Sparse training is one of the promising techniques to reduce the computational cost of deep
neural networks (DNNs) while retaining high accuracy. In particular, N: M fine-grained …

Processing-in-memory (PIM), as a novel computing paradigm, provides significant
performance benefits from the aspect of effective data movement reduction. SRAM-based …

With the prospering of mobile devices, the distributed learning approach, enabling model
training with decentralized data, has attracted great interest from researchers. However, the …

The selection of the optimizer is critical for convergence in the field of on-chip training. As
one second moment optimizer, adaptive moment estimation (ADAM) shows a significant …

Many edge/mobile devices are now able to utilize deep neural networks (DNNs) thanks to
the development of mobile DNN accelerators. Mobile DNN accelerators overcame the …

Training deep neural networks (DNNs) on edge devices has attracted increasing attention in
real-world applications for domain adaption and privacy protection. However, deploying …