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 …

Silicon Photonic interconnects are a promising technology for scaling computing systems
into the exa-scale domain. However, there exist significant challenges in terms of optical …

Modern machine learning (ML) applications are becoming increasingly complex and
monolithic (single chip) accelerator architectures cannot keep up with their energy efficiency …

We propose a new architecture called HTA for high throughput irregular HPC applications
with little data reuse. HTA reduces the contention within the memory system with the help of …

Abstract Machine learning applications have become increasingly prevalent over the past
decade across many real-world use cases, from smart consumer electronics to automotive …

In response to the burgeoning demand for high-performance computing systems, this Ph. D.
dissertation investigates the pivotal challenges surrounding Networks-on-Chip (NoCs) …

Stacking technology is an approach to improve scalability of 2D network-on-chip systems.
3D stacking technology places multiple chips vertically, while silicon chips are stacked side …

Data movement has become a limiting factor in terms of performance, power consumption,
and scalability of high-performance compute nodes with increasing numbers of processor …