Abstract
Digital systems have finite precision, which imposes a maximum bound on the accuracy of the results of the computed mathematical operations. The so-called quantization process, also wordlength optimization, aims at finding cost-efficient hardware architectures that comply with a given maximum accuracy loss. Floating-point arithmetic is commonly used to perform scientific computations because it provides high dynamic range and mathematical precision.
Highlights
Digital systems have finite precision, which imposes a maximum bound on the accuracy of the results of the computed mathematical operations
The so-called quantization process, wordlength optimization, aims at finding cost-efficient hardware architectures that comply with a given maximum accuracy loss
Certain applications require the use of dedicated hardware to achieve high computation rates and low power
Summary
Quantization of VLSI digital signal processing systems Digital systems have finite precision, which imposes a maximum bound on the accuracy of the results of the computed mathematical operations. The so-called quantization process, wordlength optimization, aims at finding cost-efficient hardware architectures that comply with a given maximum accuracy loss.
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