Engineers targeting DSP to FPGAs have traditionally used fixed-point arithmetic, mainly because of the high cost associated with implementing floating-point arithmetic. That cost comes in the form of ...
[Editor's note: For an intro to fixed-point math, see Fixed-Point DSP and Algorithm Implementation. For a comparison of fixed- and floating-point hardware, see Fixed vs. floating point: a surprisingly ...
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 ...
Floating-point arithmetic is used extensively in many applications across multiple market segments. These applications often require a large number of calculations and are prevalent in financial ...
Radar, navigation and guidance systems process data that is acquired using arrays of sensors. The energy delta from sensor to sensor over time holds the key to information such as targets, position or ...
The new Intel Xeon processors E5 v4 product family, based upon the “Broadwell” microarchitecture, is reported to deliver up to 47%* more performance across a wide range of HPC codes. To get to the ...
Yea, see topic. Using all floating point cut CPU usage in half.<BR><BR>I made a version of SineClock (the ancient BeOS program) for IRIX. I first wrote it with integer, since I figured that integer ...
Training ‘complex multi-layer’ neural networks is referred to as deep-learning as these multi-layer neural architectures interpose many neural processing layers between the input data and the ...
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