As a heterogeneous integration technology, the chiplet-based design technology integrates
multiple heterogeneous dies of diverse functional circuit blocks into a single chip by using …

Abstract As Moore's Law comes to an end, the implementation of high-performance chips
through transistor scaling has become increasingly challenging. To improve performance …

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

Computing systems are widely used in medical diagnosis, climate prediction, autonomous
vehicles, etc. As the key part of electronics, the performance of computing systems is crucial …

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 …

Chiplet-based architectures have been proposed to scale computing systems for deep
neural networks (DNNs). Prior work has shown that for the chiplet-based DNN accelerators …

The slowing and forecasted end of Moore's Law have forced designers to look beyond
simply adding transistors, encouraging them to employ other unused resources as a manner …

Chiplet-based systems integrate discrete chips on an interposer and use the interconnection
network to enable communication between different components. The topology of the …

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 …