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module example (inputA, inputB, inputC, outputA); input inputA, inputB, inputC; output outputA; tri temp; assign temp = inputA & ~inputB; // Verilog source code assign temp = inputA | ~inputB; assign outputA = temp & inputC; endmodule Net type tri is synthesizable. However, it is not advisable to use net type tri when writing synthesizable Verilog. If a node is to be driven by multiple drivers, that node should be driven only by tristate drivers. The example shown here using net type tri on node temp with multiple assign statements driving it is not a good coding method in synthesis.
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As an example of working with function curves, you animate a monorail that moves around its track, changing speeds, and stopping for passengers. To animate the monorail using function curves, follow these steps: 1. Open the Monorail.max file from the Chap 34 directory on the DVD. This file contains a simple monorail setup made from primitives. 2. Click the Play button, and watch the train move around the track. As a default, the Path Constraint s Percent track has a Linear controller that causes the train to move at a constant speed. To refine the animation, you need to change it. 3. Open the Track View Curve Editor, and locate the train group s Percent track. (You can find this track under the Objects Train Transform Path Constraint Percent menu command.) Click the Assign Controller button to open the Assign Float Controller dialog box. Select the B zier Float controller, and click OK.
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means that an external argument is required on activation but no visible output is produced, but for a trivial indication of successful termination. Such port signatures are grouped together in the layout below, in which all occurrences of 1 are dropped: R joined : 1 Date lastHire : 1 Date pay : R 1  
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fig. 5.2
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Lift state machine.
FIGURE 22.15
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continent was viewed one way. After 1492 and the visit of Christopher Columbus, the New World was viewed quite differently. Students of basic history, perhaps for mnemonic purposes, portray the historical landscape as featuring abrupt ruptures between one chunk of history, era, or epoch and the next. These historical Rubicons place signi cant importance and emphasis on hinge dates the dividing line between one era and another. Such hinges of history are a natural part of how humans make sense of the world. Chunking information in this way is not a learned strategy but is instead a fundamental aspect of the human mind. Indeed, if you don t think in hinges, you are a little unhinged. The more literary among you will recall that Shakespeare had his tragic period (the years 1601 through 1608, when he wrote Macbeth, King Lear, Hamlet, and Othello). Art lovers among us know that Picasso had his blue period (1901 to 1904). Less sympathetic observers of the IT scene would have you believe that we are only now emerging from the We suck less era of IT leadership.13 Mark Twain, a close chronicler of how Americans thought, viewed the American Civil War as a hinge of history, observing that, in the South: The war is what a.d. is elsewhere: they date from it. All day long you hear things placed as having happened since the waw; or du in the waw; or befo the waw; or right aftah the waw; or bout two yeahs or ve yeahs or ten yeahs befo the waw or aftah the waw.14 I believe we are currently standing at a hinge of history. It may not be as signi cant as the hinge that swung between living in trees and walking erect, having re and not having re, and hunting/ gathering and agriculture, but I am convinced and the rest of the book will attempt to convince you that in these last years of the rst decade of the twenty- rst century, the changes we are living through are as momentous and life changing as the dropping of the atomic bomb over Hiroshima on August 6, 1945, and the fall of the Berlin Wall on November 9, 1989. Hinges divide the world into before and after. Big hinges require major rethinking. Alan Webber, close personal friend, mentor,
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faster. The JIT compiler can store and use the table of the addresses of code of the kind p.M for each method it compiles to native code. Such optimization helps to save a lot of execution time and avoid substantial slowdown that might occur because of the use of virtual methods. 4 Basic block optimizations by JIT compilers. Typical basic block optimizations considered in Section 6.2 constant propagation and copy propagation are also performed by JIT compilers. 5 Elimination of redundant runtime checks. In many cases, the JIT compiler that sees not only the types and de nitions in the program but also all runtime structures can generate a native code without runtime checks that appear to be redundant (typically for indexing arrays). For example, consider the following typical fragment of a Java code: int [] a = new int [100]; for (int i = 0; i < 100; i++) { a[i] = i+1; } Though it is evident that no ArrayIndexOutOfBoundsException will be thrown (since the index i is guaranteed to be within the bounds of the array a), the Java compiler javac must generate the common case of the Java bytecode for the construct a[i], with the array indexing JVM command to check the value of the index at runtime, since the Java compiler should preserve Java semantics. However, such check is redundant, and the JIT compiler is in a good position to generate more optimal native code for this construct, without bound checks for the index i. More details on JIT compilers later in 8. EXERCISES TO CHAPTER 6 6.1 Design and implement a suitable representation of basic blocks, and implement the typical basic block optimizations: constant folding, constant propagation, copy propagation, and dead code elimination summarized in Section 6.3. 6.2 Design and implement an algorithm of transforming the program to its SSA form. 6.3 Implement the algorithm of compaction of the constant pool of a Java .class le outlined in Section 6.9. 6.4 Practice with some existing open source JIT compiler, for example, the FJIT compiler in Shared Source CLI/Rotor noncommercial version of .NET), or the JIT Java compiler in Sun s OpenJDK: Add to the code of the compiler any optimizations (e.g., basic block optimization, devirtualization, etc.) outlined in Section 6.10.
70.6 5.6 3.9 66.7
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