In computer architecture, significant innovation frequently comes from industry. However, the
simulation tools used by industry are often not released for open use, and even when they …

Graphics Processing Units (GPUs) are rapidly dominating the accelerator space, as
illustrated by their wide-spread adoption in the data analytics and machine learning markets …

Computing systems have undergone a tremendous change in the last few decades with
several inflexion points. While Moore's law guided the semiconductor industry to cram more …

In this paper we focus on knowledge extraction from large-scale wireless networks through
stream processing. We present the primary methods for sampling, data collection, and …

The design of specialized architectures for accelerating the inference procedure of Deep
Neural Networks (DNNs) is a booming area of research nowadays. While first-generation …

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without
decrypting it. FHE has garnered significant attention over the past decade as it supports …

The past year has witnessed the increasing popularity of Large Language Models (LLMs).
Their unprecedented scale and associated high hardware cost have impeded their broader …

A trojan backdoor is a hidden pattern typically implanted in a deep neural network (DNN). It
could be activated and thus forces that infected model to behave abnormally when an input …

Scalable server-grade non-volatile RAM (NVRAM) DIMMs became commercially available
with the release of Intel's Optane DIMM. Recent studies on Optane DIMM systems unveil …

Today, DNNs' high computational complexity and sub-optimal device utilization present a
major roadblock to democratizing DNNs. To reduce the execution time and improve device …