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 ...
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 ...
Targeting automotive radar, among other applications, Texas Instruments (www.ti.com) has introduced a family of fixed-point digital signal controllers that are hardware- and software-compatible with ...
Automotive Advanced Driver Assistance Systems (ADAS) applications are increasingly demanding radar modules with better capability and performance. These applications require sophisticated radar ...
Many numerical applications typically use floating-point types to compute values. However, in some platforms, a floating-point unit may not be available. Other platforms may have a floating-point unit ...
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 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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