Without a doubt, many algorithms can benefit from employing a floating-point implementation. The code can be simpler and take fewer cycles to execute than fixed-point implementations. However, these ...

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 ...

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 ...