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k=8*sizeof(sub_operand);m=((2^s)-1)<mask=~(m|m<

// computationally-intensive loop using soft SIMD{  //somewhere in this loop, perform a SIMD shift:  A=(A>>s) & mask;}

So how much of a speed-up can soft SIMD shifts give you? If you're shifting four operands by a constant shift amount and the operands are already packed into a register, then you'll need just one shift and one and per four operands (rather than one shift per operand if you're not using soft SIMD). Assuming that shifts and ands require the same number of instruction cycles, this approach translates into a 2X speedup in the inner loop (which doesn't count the overhead of mask generation and packing/unpacking operands). The overhead will vary based on the processor and on the arrangement of data in memory, but you're still likely to end up with a significant performance improvement.

Arithmetic left shifts are identical to logical left shifts. Arithmetic right shifts, however, are more complicated because you need to manage the signs of the operands. You can generate a sophisticated, data-dependent mask to do this, but it usually isn't worth the effort—the overhead typically outweighs the benefit.

Soft-SIMD addition Soft SIMD can also be used for implementing addition. The exact implementation approach will vary depending on whether the operands are signed or unsigned; typically you'll need to either limit the operands so that the additions won't generate a carry, and/or define guard bits” to ensure that carries aren't propagated into adjacent operands.For example, if the addition operands are all unsigned, the register is 16 bits wide, and each operand is 8 bits, each addition must be limited to the [0…127]+[0…128] range. (Otherwise the carry propagation corrupts the results of an adjacent operand.) Once you've packed the operands into the registers and dealt with the carry issue, you simply add the full-width registers to generate multiple results.

An example of implementing soft SIMD additions is shown in Figure 2 , where 16-bit registers are packed with two data operands each. Here, the operands are all negative, and are limited to 7 bits (including the sign bit). The eighth bit is used as a guard bit to avoid carry propagation in the result; all four input guard bits must be set to zero prior to the addition. The result range is limited to [-64(0x40) … -1(0x7F)] + [-64…0]. After the addition, the guard bit is set to 1 to represent the sign of the negative result.

2. Using soft SIMD to perform two simultaneous additions of negative operands. There are two operands packed into each 16-bit register; each operand (A0, A1, B0, B1) must be limited to fit within seven bits (including the sign bit) to avoid carry propagation. The result guard bits are actually the sign bits.

It might alsobe possible to use Java for integration and customization functions forscripting, but unfortunately the Java language is not well-tailored tothese tasks. A scripting language like Tcl allows these tasks to becarried out much more easily and by less sophisticated developers.

An alternative is to embed one or more microprocessor cores directly into the main FPGA fabric. One, two, and even four core implementations are currently available as I pen these words (Fig 14 ).

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k=8*sizeof(sub_operand);m=((2^s)-1)<mask=~(m|m<

// computationally-intensive loop using soft SIMD{  //somewhere in this loop, perform a SIMD shift:  A=(A>>s) & mask;}

So how much of a speed-up can soft SIMD shifts give you? If you're shifting four operands by a constant shift amount and the operands are already packed into a register, then you'll need just one shift and one and per four operands (rather than one shift per operand if you're not using soft SIMD). Assuming that shifts and ands require the same number of instruction cycles, this approach translates into a 2X speedup in the inner loop (which doesn't count the overhead of mask generation and packing/unpacking operands). The overhead will vary based on the processor and on the arrangement of data in memory, but you're still likely to end up with a significant performance improvement.

Arithmetic left shifts are identical to logical left shifts. Arithmetic right shifts, however, are more complicated because you need to manage the signs of the operands. You can generate a sophisticated, data-dependent mask to do this, but it usually isn't worth the effort—the overhead typically outweighs the benefit.

Soft-SIMD addition Soft SIMD can also be used for implementing addition. The exact implementation approach will vary depending on whether the operands are signed or unsigned; typically you'll need to either limit the operands so that the additions won't generate a carry, and/or define guard bits” to ensure that carries aren't propagated into adjacent operands.For example, if the addition operands are all unsigned, the register is 16 bits wide, and each operand is 8 bits, each addition must be limited to the [0…127]+[0…128] range. (Otherwise the carry propagation corrupts the results of an adjacent operand.) Once you've packed the operands into the registers and dealt with the carry issue, you simply add the full-width registers to generate multiple results.

An example of implementing soft SIMD additions is shown in Figure 2 , where 16-bit registers are packed with two data operands each. Here, the operands are all negative, and are limited to 7 bits (including the sign bit). The eighth bit is used as a guard bit to avoid carry propagation in the result; all four input guard bits must be set to zero prior to the addition. The result range is limited to [-64(0x40) … -1(0x7F)] + [-64…0]. After the addition, the guard bit is set to 1 to represent the sign of the negative result.

2. Using soft SIMD to perform two simultaneous additions of negative operands. There are two operands packed into each 16-bit register; each operand (A0, A1, B0, B1) must be limited to fit within seven bits (including the sign bit) to avoid carry propagation. The result guard bits are actually the sign bits.

It might alsobe possible to use Java for integration and customization functions forscripting, but unfortunately the Java language is not well-tailored tothese tasks. A scripting language like Tcl allows these tasks to becarried out much more easily and by less sophisticated developers.

It might alsobe possible to use Java for integration and customization functions forscripting, but unfortunately the Java language is not well-tailored tothese tasks. A scripting language like Tcl allows these tasks to becarried out much more easily and by less sophisticated developers.

An alternative is to embed one or more microprocessor cores directly into the main FPGA fabric. One, two, and even four core implementations are currently available as I pen these words (Fig 14 ).

Assume:R1 <>2 <>3 C4 <>1 <>3

Next installment: The Intel HD Audio Codec Layer and Vista details.

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An alternative is to embed one or more microprocessor cores directly into the main FPGA fabric. One, two, and even four core implementations are currently available as I pen these words (Fig 14 ).

Assume:R1 <>2 <>3 C4 <>1 <>3

Next installment: The Intel HD Audio Codec Layer and Vista details.

Where:A1 = R3 C2A2 = R2 R3 C2 C3A3 = R2 (C2 + C3 s = jω

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Assume:R1 <>2 <>3 C4 <>1 <>3

Next installment: The Intel HD Audio Codec Layer and Vista details.

Where:A1 = R3 C2A2 = R2 R3 C2 C3A3 = R2 (C2 + C3 s = jω

There are many different types of thermal imaging technologies. The first technology introduced over three decades ago required cryogenic cooling to operate and were extremely expensive. Such cooled detectors” are still in use today, typically in military applications.

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Next installment: The Intel HD Audio Codec Layer and Vista details.

Where:A1 = R3 C2A2 = R2 R3 C2 C3A3 = R2 (C2 + C3 s = jω

Where:A1 = R3 C2A2 = R2 R3 C2 C3A3 = R2 (C2 + C3 s = jω

There are many different types of thermal imaging technologies. The first technology introduced over three decades ago required cryogenic cooling to operate and were extremely expensive. Such cooled detectors” are still in use today, typically in military applications.

Distributed CEF In Distributed CEF (dCEF), line cards, such as the 7500 series Versatile Interface Processor (VIP) cards or Gigabit Switch Router (GSR) line cards, maintain an identical copy of the FIB and adjacency tables. The line cards perform the express forwarding between port adapters, relieving the route processor of any involvement in the switching operation. Figure 16 illustrates dCEF mode operation.

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There are many different types of thermal imaging technologies. The first technology introduced over three decades ago required cryogenic cooling to operate and were extremely expensive. Such cooled detectors” are still in use today, typically in military applications.

Distributed CEF In Distributed CEF (dCEF), line cards, such as the 7500 series Versatile Interface Processor (VIP) cards or Gigabit Switch Router (GSR) line cards, maintain an identical copy of the FIB and adjacency tables. The line cards perform the express forwarding between port adapters, relieving the route processor of any involvement in the switching operation. Figure 16 illustrates dCEF mode operation.

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