HOUSTON, February 19, 2008 — Embedded control engineers prefer the simplified programming provided by floating-point architectures, but are often deterred by the higher cost. Addressing this paradox, ...
In a recent survey conducted by AccelChip Inc. (recently acquired by Xilinx), 53% of the respondents identified floating- to fixed-point conversion as the most difficult aspect of implementing an ...
If you are used to writing software for modern machines, you probably don’t think much about computing something like one divided by three. Modern computers handle floating point quite well. However, ...
Most of the algorithms implemented in FPGAs used to be fixed-point. Floating-point operations are useful for computations involving large dynamic range, but they require significantly more resources ...
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 ...
Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks today are often ...
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