Floating-point arithmetic is used extensively in many applications across multiple market segments. These applications often require a large number of calculations and are prevalent in financial ...

AMD researchers argue that, while algorithms like the Ozaki scheme merit investigation, they're still not ready for prime ...

The rise in interest in deep learning chips for training and inference has reignited interest in how reduced precision compute can cut down on energy, bandwidth, and other constraints inherent to ...

Native Floating-Point HDL code generation allows you to generate VHDL or Verilog for floating-point implementation in hardware without the effort of fixed-point conversion. Native Floating-Point HDL ...

Training deep neural networks is one of the more computationally intensive applications running in datacenters today. Arguably, training these models is even more compute-demanding than your average ...