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 …

In pursuit of higher inference accuracy, deep neural network (DNN) models have
significantly increased in complexity and size. To overcome the consequent computational …

Chiplet-based convolution neural network (CNN) accelerators have emerged as a promising
solution to provide substantial processing power and on-chip memory capacity for CNN …

The complexity and size of recent deep neural network (DNN) models have increased
significantly in pursuit of high inference accuracy. Chiplet-based accelerator is considered a …

Thermal analysis is an essential step that enables co-design of the computing system (ie,
integrated circuits and computer architectures) with the cooling system (eg, heat sink) …

Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's
Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing. However …

2.5 D chiplet systems have been proposed to improve the low manufacturing yield of large-
scale chips. However, connecting the chiplets through an electronic interposer imposes a …

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

2.5 D chiplet systems have showcased low manufacturing costs and modular designs for
machine learning (ML) acceleration. Nevertheless, communication challenges arise from …

Phase Change Memory (PCM) is an attractive candidate for main memory, as it offers non-
volatility and zero leakage power while providing higher cell densities, longer data retention …