This paper updates the survey of AI accelerators and processors from past three years. This
paper collects and summarizes the current commercial accelerators that have been publicly …

Special-purpose hardware accelerators are increasingly pivotal for sustaining performance
improvements in emerging applications, especially as the benefits of technology scaling …

Convolution models with long filters have demonstrated state-of-the-art reasoning abilities in
many long-sequence tasks but lag behind the most optimized Transformers in wall-clock …

The design space exploration of scaled-out manycores for communication-intensive
applications (eg, graph analytics and sparse linear algebra) is hampered due to either lack …

Numerical algorithms to solve mathematical optimization problems efficiently are essential to
applications in many areas of engineering and computational science. To solve optimization …

This paper presents Morpher, an open-source endto-end compilation and simulation
framework, to assist design space exploration and application-level developments of CGRA …

Coarse-grained reconfigurable architecture (CGRA) is a promising accelerator design
choice due to its high performance and power efficiency in the computation or data-intensive …

Extremely high data rates at modern synchrotron and X-ray free-electron laser light source
beamlines motivate the use of machine learning methods for data reduction, feature …

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

Philosophically, our approaches to acceleration focus on the extreme. We must optimise
accelerators to the maximum, leaving software to fix any hardware-software mismatches …