Loop unrolling

As an example of transformation. Figure 5 shows a partial loop unrolling applied to EXAMPLE. The simplified dependency graph (Figure 4) was duplicated twice, and only the real dependencies are shown. Operation 1 of the second iteration does not depend on the result D of operation 8, which is only used by operation 5. Similarly, only operation 6 uses the result C of operation 7. Thus, operations 7 and 8 can be executed in parallel with the test at the beginning of the next loop iteration (operation 1). The partially-unrolled dependency graph captures this information. 

Supports behavioral transformations, primarily to improve the efficiency of the control structure constant folding, common subexpression elimination, dead procedure elimination, inline expansion and formation of procedures, code motion into and out of the branches of decoding operations, and loop unrolling. 

Applies optimizing compiler transformations, including constant folding, common subexpression elimination, dead code elimination, loop retiming, loop pipelining, loop unrolling, and loop jamming. 

For-loop unrolling, where fixed-iteration loops are unrolled to increase the scope of the optimizations. 

Loop unrolling—transforming a loop into a linear sequence by making copies of the loop body—illustrates how automata-theoretic algorithms implement structural transformations. Consider a machine which executes a definite loq) (one whose iteration bounds are known at compile time) such a machine can be built from a sequencer and a counter as shown in Figure 7. The sequencer resets the counter at the start of the loop, increments it at the end of each iteration, and exits the loop when the counter reaches its limit. The loop body performs a simple assignment The state transition table for the sequencer component is shown in Figure 8 we have used more states than necessary in the sequencer to more clearly illustrate how the loop unrolls. 

For-loop unrolling. Fixed-iteation loops are unrolled to ineease the scope of subsequent optimizations. This is accomplished in BIF by replacing the For-loop node with a Block node, where each child of the block node is a duplicate of the loop body for a particular value of loop index. The example below illustrates the lo( unrolling transformation. 

Schmidt-Lantermann clefts are structures where the cytoplasmic surfaces of the myelin sheath have not compacted to form the major dense line and therefore contain Schwann or glial cell cytoplasm (Fig. 4-9). They are common in peripheral myelin but rare in the CNS. These inclusions of cytoplasm are present in each layer of myelin. The clefts can be visualized in the unrolled myelin sheet as tubes of cytoplasm similar to the tubes making up the lateral loops but in the middle regions of the sheet, rather than at the edges (Fig. 4-9). 

The for-loop statement is the one typically supported for synthesis. A for-loop is implemented by unrolling the for-loop, that is, all statements within the for-loop are replicated, once for each value of the for-loop index. This puts a restriction on the for-loop bounds, which must therefore evaluate to constants. Here is an example of a for-loop statement. 

Fire blankets For clothing fires, use the wall-mounted blanket accessible with a loop for inserting your arm. Insert your arm and rotate your body such that the container opens and the blanket wraps around you. For benchtop fires, remove the blanket from the wall-mounted canister through the bottom door, unroll, and cover the fire. 

For the time being, we limit ourselves to models of a fixed, statically known size. This excludes generic parameters and unconstrained arrays in VHDL. Likewise, loops are restricted to the form for and the number of iterations must be known statically in the rest of this paper, we shall assume the loops to be unrolled in a pre-processing phase, and shall not consider them in our discussion. 

When binding is finished, all information is available for programming CBBs and state machines. The ASIC emulator can be customized and control code can be generated. In order to keep the controller complexity as low as possible, all loops are unrolled at the end of binding. This procedure is justifiable, since the size of the ASIC emulator has no relation to the final ASIC implementation. Note that the goal is to get some performance indications with this board. 

Interestingly, the effort invested in developing vectorizable algorithms pays off on scalar architectures as well. The CPU ratio between the nested-loop algorithm and the algorithm just presented is slightly more favorable for the latter on vector machines since the nested DO loop constructs of Section III.D contain comparatively more or deeper IP s and low-count do s that must be unrolled. 

Automatically applies block-level transformations to produce the dacon with the fastest implementation. Transformations supported merge adjacent bloi s, combine a block followed by two or more conditionally executed blocks into a single block, unroll loops, and move the test at the top of a loop to the bottom of the loop. 

Uses transformations to eliminate useless registers, replace addition by 1 with an increment operation, unroll loops, and perform dead code elimination, etc. 

A prime example is the range of a For loop. Unless the compiler is given static range parameters, it caimot determine how many times to unroll the loop and therefore how many copies of the hardware to construct. A simulator is much less concerned with such issues as it will take the present values of the range parameters and repeat (tie loop as many times as they specify. 

So let us look at the multiplication operation. First, another type of loop is being used. The Infinite Loop is not always supported by sjoithesizers owing to the unpredictable nature of their operation - the loop could be unrolling forever if a condition that causes an exit is never met. Similarly, the While Loop is often not supported for synthesis. The s)mtax of bodi these alternative loop types is shown in Box 6.11. This loop is controlled by the counter I, which increments on each pass around the loop. When I reaches the n - the number of bits in A - the Exit statement quits the loop. This statement does not necessarily require the loop label in this example but one should be used when embedding loops to darify which loop is to be stopped. Next is another loop control statement that will immediately start the next iteration of the loop, assuming its condition is met. They are both discussed in Box 6.12. 

The S3mthesis of the multiplier will involve the unrolling of the loop. A 2 x2 multiplication means that two identical blocks of hardware will be created to represent the operations in the loop. Just as the synthesizer did not know that it was creating adder circuits with the user-defined ripple-carry adder examples, it does not know that this circuit is a multiplier. Instead, it simply carries out a series of addition and shift operations. Area optimization is Aerefore die only way to spot common elements and combine them. 

The logic described in an N-times loop will be replicated N times as the loop is unrolled. 

We now consider the case when the traversed node is a loop with a single-bit condition variable exit, i.e. exit = 1. Let X(node) denote modified variables in the loop body. Note that all loops with fixed iteration bounds have already been unrolled. Therefore, node is a data-dq)endent loop such as a while loop. 

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