With the end of Moore's Law and Dennard scaling, it has become increasingly difficult to
implement high-performance computing systems on a monolithic chip. The chiplet …

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 …

The Chiplet methodology can accelerate VLSI system development and provide better
flexibility. However, it is not easy to build interconnection networks across multiple chiplets …

The increased computational capability in heterogeneous manycore architectures facilitates
the concurrent execution of many applications. This requires, among other things, a flexible …

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing
hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x …

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

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

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 …

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