Reconfigurable architectures are today experiencing a renewed interest for their ability to
provide specialization without sacrificing the capability to adapt to disparate workloads …

Over the past decade, a massive proliferation of machine learning algorithms has emerged,
from applications for surveillance to self-driving cars. The turning point occurred with the …

The growing numbers of application areas for artificial intelligence (AI) methods have led to
an explosion in availability of domain-specific accelerators, which struggle to support every …

The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …

ML accelerators have largely focused on building general platforms for deep neural
networks (DNNs), but less so on shallow machine learning (SML) algorithms. This paper …

In the data-centric era, interconnected devices must be able to communicate efficiently and
securely with their hosts even over untrusted networks. This led to the adoption of several …

Edge systems are required to autonomously make real-time decisions based on large
quantities of input data under strict power, performance, area, and other constraints. Meeting …

Cutting edge detectors push sensing technology by further improving spatial and temporal
resolution, increasing detector area and volume, and generally reducing backgrounds and …