The growing importance and compute demands of artificial intelligence (AI) have led to the
emergence of domain-optimized hardware platforms. For example, Nvidia GPUs introduced …

The advent of AI has driven the adoption of high density low precision arithmetic on FPGAs.
This has resulted in new methods in mapping both arithmetic functions as well as dataflows …

Narrow bit-width data formats are key to reducing the computational and storage costs of
modern deep learning applications. This paper evaluates Microscaling (MX) data formats that …

Arithmetic is a key component and is ubiquitous in today’s digital world, ranging from
embedded to highperformance computing systems. With machine learning at the fore in a wide …

Current soft processor architectures for FPGAs do not utilize the potential of the massive
parallelism available. FPGAs now support many thousands of embedded floating point operators…

Interactive intelligent services, such as smart web search, are important datacenter workloads.
They rely on dataintensive deep learning (DL) algorithms with strict latency constraints …

This paper describes architectural enhancements in Intel® Agilex™ FPGAs and SoCs. Agilex
devices are built on Intel's 10nm process and feature next-generation programmable fabric…

With the advent of machine learning as perhaps the most high-profile application area for
FPGAs, there is a compelling reason to improve the provision of smaller precision arithmetic on …

This work describes the architecture of a new FPGA DSP block supporting both fixed and
floating point arithmetic. Each DSP block can be configured to provide one single precision …

Arithmetic based applications are one of the most common use cases for modern FPGAs.
Currently, machine learning is emerging as the fastest growth area for FPG As, renewing an …