In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …

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 …

Meta has traditionally relied on using CPU-based servers for running inference workloads,
specifically Deep Learning Recommendation Models (DLRM), but the increasing compute …

Attention mechanisms, primarily designed to capture pairwise correlations between words,
have become the backbone of machine learning, expanding beyond natural language …

This paper presents Xel, a cloud-agnostic data platform for the design-driven building of
high-availability data science services as a support tool for data-driven decision-making. We …

As model sizes in machine learning continue to scale, distributed training is necessary to
accommodate model weights within each device and to reduce training time. However, this …

Increasing deployment of Deep Neural Networks (DNNs) recently fueled interest in the
development of specific accelerator architectures capable of meeting their stringent …

Reconfigurable accelerators for deep neural networks (DNNs) promise to improve
performance such as inference latency. STONNE is the first cycle-accurate simulator for …

Major device nodes within the Internet of Things (IoT) system collects and store information
in bit forms of 0's and 1's regardless of its repetition. The nodes do not possess the capability …

Wireless communications at the chip scale emerge as a interesting complement to
traditional wire-based approaches thanks to their low latency, inherent broadcast nature …