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 ...

Migrating signal-processing algorithms from floating- to fixed-point is often necessary to meet various design constraints, including real-time performance, cost and power dissipation. The migration ...

Algorithms for working with floating point numbers are complex and very easy to get wrong. For example, consider the algorithm for adding two floating point numbers. You can’t simply add the ...

Today’s digital signal processing applications such as radar, echo cancellation, and image processing are demanding more dynamic range and computation accuracy. Floating-point arithmetic units offer ...

To implement algorithms, digital-signal-processing (DSP) developers have had to rely upon design flows that use manual approaches. Aside from being time consuming, the manual process can invite errors ...

SAN JOSE, Calif., 27 Sept. 2010 Freescale Semiconductor Inc. in Austin, Texas, will incorporate its AltiVec vector processing technology into its multicore QorIQ microprocessor family later this year ...

As defined by the IEEE 754 standard, floating-point values are represented in three fields: a significand or mantissa, a sign bit for the significand and an exponent field. The exponent is a biased ...

Why floating point is important for developing machine-learning models. What floating-point formats are used with machine learning? Over the last two decades, compute-intensive artificial-intelligence ...

Fixed point may be simple, but it is very limited in the range of numbers it can represent. If a calculation involves big scale changes, i.e. with results very much smaller or bigger than the initial ...

Researchers have released qmatmul, a Python package whose algorithm halves the floating-point multiplications needed for ...