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Hardware and Computer Organization- P9

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Notice that the END directive comes at the end of all of the source code, not just the program code. It is very fundamental to all of the programming concepts that we’ve covered so far.. Branches and the general process of conditional code execution based upon the state of the flags in the CCR.. The primary addressing modes of the...

Hardware and Computer Organization- P10

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We’ve already looked at the archi- tecture from the point of view of the instruction set, the addressing. In other words, what is the effective address of the operand(s).. Consider the form of the instructions shown below:. That is, move the contents of the memory location specified by the source EA to the memory location specified by the destination EA....

Hardware and Computer Organization- P11

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We see this in the next two bytes of the instruction, AA 00. The r/m field value 001 then gives us the final form of the address calculation, [BX + DI + DISP]. The final byte of the instruction, 55h, is the 8-bit immediate value, imm8, as shown in the format of the opcode.. Also, some of the instructions will...

Hardware and Computer Organization- P12

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1 Another feature of the compare instructions is that they will always set the flags, so the state of the S bit is ignored.. The load/store operations must also deal with the size and type of the operands, since bytes and half-words are also permitted.. 1 There’s a wonderful story about the intrepid hobbyists/pioneers of the PC industry. The load...

Hardware and Computer Organization- P13

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The operation of the circuit is very straightforward.. The output of the D/A. The output is applied to the minus input of the comparator. The voltage that we want to digitize is applied to the positive input of the comparator. However, we also check the output of the comparator to see if it changed from 1 to 0. When the...

Hardware and Computer Organization- P14

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SRAM memory is based on the principle of the cross-coupled, inverting logic gates. It is easily apparent that the capacity increases and the speed of the memory decreases at each level of the hierarchy. Figure 14.1: The memory hierarchy. Capacity of the memory at each level CPU. L2 Main memory. Main memory 1 M–1.5 Gbyte (30 ns). As we’ve seen,...

Hardware and Computer Organization- P15

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All of the processing require- ments have been placed back onto the PC in the printer drivers.. It is up to the system architects and designers to decide upon the partitioning of the algorithm between software (slow, low-cost and flexible) and hardware. We can use object oriented design methodology and UML-based tools to generate C++ or an HDL source file...

Hardware and Computer Organization- P16

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Predictions are that we will easily be at 5 GHz in the next year or so, and that 10 GHz is not far behind.. A modern microprocessor is about ¾ of an inch on a side, so this means that 62% of the clock period will be wasted just getting the clock signal from one edge of the chip to...

Hardware and Computer Organization- P17

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Chapter 9: Solutions for Odd-Numbered Problems. This is an example of the addressing mode known as “address register indirect with index and displacement”. The effective address is the sum of the address value in A0, the index value, D0, and the 2’s complement displacement. The program could still be relocatable by managing what gets loaded into A0 and D0, but...

Hardware and Computer Organization- P18

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ELSEVIER SCIENCE DVD-ROM LICENSE AGREEMENT. PLEASE READ THE FOLLOWING AGREEMENT CAREFULLY BEFORE USING THIS DVD-ROM PRODUCT. THIS DVD-ROM PROD- UCT IS LICENSED UNDER THE TERMS CONTAINED IN THIS DVD-ROM LICENSE AGREEMENT (“Agreement. BY USING THIS DVD-ROM PRODUCT, YOU, AN INDIVIDUAL OR ENTITY INCLUDING EMPLOYEES, AGENTS AND REPRESENTATIVES (“You” or “Your. ACKNOWLEDGE THAT YOU HAVE READ THIS AGREEMENT, THAT YOU UNDERSTAND...

Hardware Acceleration of EDA Algorithms- P1

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This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they...

Hardware Acceleration of EDA Algorithms- P2

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2.2 Logic block in the FPGA. 3.2 Hardware model of the NVIDIA GeForce GTX 280. 3.3 Memory model of the NVIDIA GeForce GTX 280. 4.1 Abstracted view of the proposed idea. 4.3 State diagram of the decision engine. 4.4 Signal interface of the clause cell. 4.5 Schematic of the clause cell. 4.6 Layout of the clause cell. 4.7 Signal interface...

Hardware Acceleration of EDA Algorithms- P3

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It is estimated that the annual revenue loss due to IP infringement in the IC industry is in excess of $5 billion [42]. FPGAs, because of their re-programmability, are becoming very popular for cre- ating and exchanging VLSI IPs in the reuse-based design paradigm [27]. The emerging trend is that most IP exchange and reuse will be in the form...

Hardware Acceleration of EDA Algorithms- P4

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Figure 4.3 shows the state machine of the decision engine. The decision engine assigns the variables in the order of their identification tag, which is a numerical ID for each variable, statically assigned such that most commonly occurring variables are assigned a lower tag. If there is a conflict, all the variables participating in the conflict clause are communicated by...

Hardware Acceleration of EDA Algorithms- P5

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Cook, S.: The complexity of theorem-proving procedures. In: Proceedings of the Design Automation Conference, pp. Papadimitriou, C.H., Wolfe, D.: The complexity of facets resolved. ’02: Proceedings of the 10th Annual IEEE Symposium on Field-Programmable Custom Com- puting Machines, p. In: Proceedings of the International Conference on Computer-Aided Design (ICCAD), pp. In: ACSC ’02: Proceedings of the. In our approach, clause...

Hardware Acceleration of EDA Algorithms- P6

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The third term represents the number of bits required to record the index of the bin in which the variable was assigned or implied, which requires as many bits as the logarithm of the number of bins (log 2 ( A C tot. Solving the above equation, using a maximum number of variables (V tot ) of 10K, gives C...

Hardware Acceleration of EDA Algorithms- P7

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Accelerating Fault Simulation on a Graphics Processor. In today’s complex digital designs, with possibly several million gates, the number of faulty variations of the design can be dramatically higher. Fault sim- ulation is an important but expensive step of the VLSI design flow, and it helps to identify faulty designs. The ratio of F sim to the total number of...

Hardware Acceleration of EDA Algorithms- P8

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8.4 Our Approach 123 offered by GPUs, our implementation of the gate evaluation thread uses a memory lookup-based logic simulation paradigm.. Fault simulation of a logic netlist consists of multiple logic simulations of the netlist with faults injected on specific nets. Then we discuss (iv) the implementation of fault simulation for a circuit. The output of the simulation of a...

Hardware Acceleration of EDA Algorithms- P9

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The implementation of the computation of detectabilities and cumulative detectabilities in FSIM ∗ and GFTABLE is different, since in GFTABLE, all compu- tations for computing detectabilities and cumulative detectabilities are done on the GPU, with every kernel executed on the GPU launched with T threads. In FSIM∗, the backtracing is performed in a topological manner from the output of the...

Hardware Acceleration of EDA Algorithms- P10

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This is because the GPU mem- ory latencies can be better hidden when more device evaluations are issued in parallel.. We first converted all the double precision computa- tions in the BSIM3 code into single precision before modifying it for use on the GPU. A large fraction (on average 75%) of the SPICE runtime is spent in evaluating transistor model...