By Phil Lapsley, Jeff Bier, Amit Shoham, Edward A. Lee
ISBN-10: 0780334051
ISBN-13: 9780780334052
This state of the art, sensible consultant brings you an self sustaining, entire creation to DSP processor expertise. a radical instructional and review of DSP architectures, this ebook encompasses a large diversity of todays product choices in examples that illustrate DSP positive factors and functions. This publication is mainly important to digital platforms designers, processor architects, engineering managers, and product planners.From the again CoverElectrical Engineering/Signal Processing DSP Processor basics Architectures and lines A quantity within the IEEE Press sequence on sign Processing Jerry D. Gilbson, sequence Editor because the emergence of the 1st commercially winning electronic sign processors within the early Eighties, the programmable DSP industry has burgeoned. Designers can now choose from an enormous array of specialised processors with strong signal-processing functions. DSP Processor basics provides an self reliant, entire advent to DSP processor expertise. an intensive instructional and assessment of DSP architectures, this e-book encompasses a extensive diversity of today’s product choices in examples that illustrate DSP good points and services. This booklet is mainly invaluable to digital structures designers, processor architects, engineering managers, and product planners. issues coated include:•Numeric formats•Data paths•Memory structures•Instruction sets•Execution control•Pipelining•On-chip peripherals•On-chip debugging facilities•Clocking options•And more!sharingmatrix.com
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Extra resources for DSP Processor Fundamentals: Architectures and Features
Example text
An improved rounding scheme that addresses the bias problem of the round-to-nearest approach is convergent rounding. Convergent rounding is slightly more sophisticated than the familiar round-to-nearest technique. In convergent rounding, when a number to be rounded lies exactly at the midpoint between the two nearest output values, it may be rounded higher or lower. The rounding direction depends on "the value of the bit of the number that is in the position that will become the least significant bit (LSB) of the output word.
When this is done, the most significant N/2+ 1 bits of the N-bit result are all equal to the value of the sign bit (they are sometimes called sign exten- Sign Bit \ 7 Integer Bits Radix Point Operand A Sign Bit I Operand B 15 Integer Bits Result Select Subset Output Word 1 Sign Bit 1 7 Integer Bits FIGURE 4-2. When mUltiplying integer values, the result is also an integer, with twice the overall width of the input operands. Normally, the programmer selects the least significant half of the result word for use in the next step of processing.
The greater the possible quantization error relative to the size of the value represented, the less precision is available. For a fixed-point format, we define the maximum available precision to be equal to the number of bits in the format. For example, a 16-bit fractional format provides a maximum 16 bits of precision. This definition is based on computing the ratio of the size of the value represented to the size of the maximum quantization error that could be suffered when converting from a more precise representation via rounding.