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Popular since the 1970s for general-purpose computing and supercomputing, floating point is increasingly in demand for compute-intensive digital signal processing applications like sensor fusion, equalization, vision processing, radar processing, and multi-antenna MIMO. For the type of signal processing required by these applications (think Kalman filtering, IIR filters, and multiple signal classification, or MUSIC, algorithms in radar), floating point provides the best performance and sometimes lower power consumption. Today, Cadence launched the latest optimizable Tensilica processor, the Xtensa LX7 (Figure 1), which increases floating-point scalability from 2 to 64 FLOPS/cycle for a broad portfolio of DSPs that are used in many markets, including: convolutional neural networks (CNNs), autonomous cars, and internet of things (IoT) devices.
What else is driving the increased use of floating point? Here are five key trends, with a look at how the Xtensa LX7 is addressing each.
As an application example, consider a typical radar signal processing module. Such modules rely on direction of arrival algorithms like MUSIC, which require computing eigenvalues and eigenvectors of matrices, roots of polynomials, and kernels sensitive to data precision and hard-to-predict dynamic range. For these characteristics, floating point is ideal.
The Xtensa LX7 is the basis for the new Tensilica Vision P6 DSP for image, computer vision, and CNN processing, the new Tensilica Fusion G3 DSP for multi-purpose fixed- and floating-point applications, and the ConnX BBE DSPs for baseband and radar applications. Read the Xtensa LX7 datasheet for more details.