Deep learning has revolutionized many sectors of industry and daily life, but as application
scale increases, performing training and inference with large models on massive datasets is …

With the advent of Deep Neural Network (DNN) accelerators, permanent faults are
increasingly becoming a serious challenge for DNN hardware accelerator, as they can …

The surging demand for machine learning (ML) applications has emphasized the pressing
need for efficient ML accelerators capable of addressing the computational and energy …

Monolithic in-memory computing (IMC) architectures face significant yield and fabrication
cost challenges as the complexity of DNNs increases. Chiplet-based IMCs that integrate …

Large-scale deep neural network (DNN) accelerators are poised to facilitate the concurrent
processing of diverse DNNs, imposing demanding challenges on the interconnection fabric …

The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning
method for large language models (LLM), which is favored due to its ability to sparsify and …

Interposer and chiplet-based 2.5-D integrated circuit (IC) designs have become a new trend
for block-level heterogeneous integration. In this paper, a new hybrid metaheuristic …

In shared environments like cloud-based datacenters, hardware accelerators are deployed
to meet the scale-out computation demands of deep neural network (DNN) inference tasks …

The continuous increase in GPU performance benefits a wide range of high-performance
computing (HPC) applications. Slower growth of transistor density and limited size of chip …

To enhance the precision of inference, deep neural network (DNN) models have been
progressively growing in scale and complexity, leading to increased latency and …