Some features of the site may not work correctly. This paper presents a comparative study of CMOS static and dynamic logic. Effect of voltage variation on power and delay of static and dynamic CMOS logic styles studied. But the dynamic… Expand. Save to Library Save.
Create Alert Alert. Share This Paper. Background Citations. Methods Citations. Figures and Tables from this paper. Citation Type. Has PDF. Publication Type. More Filters. A new approach for constant delay logic style is developed in this paper to provide improved power and delay named Low Power High Speed logic LP-HS logic.
Constant delay logic style is examined … Expand. View 1 excerpt, cites background. Effect of CD logic on multiplier accumulator unit. There are different logic families exist for an efficient performance of the circuits. View 1 excerpt, cites methods. A modified approach for constant delay logic style is developed in this paper to provide improved power and delay named LP-HS logic. To design a VLSI circuit having low power and fast execution or high speed is the most testing … Expand.
Highly Influenced. View 4 excerpts, cites background. Testing and planning of very large scale integration circuits are the most challenging aspects now days, whenever a chip manufacture takes place many important parameters like doping of substrate, … Expand.
View 2 excerpts, cites methods. View 1 excerpt. Sub-threshold is a new paradigm in the digital VLSI design today. In this way the predictor does not predict a program behaviour based on previous execution of the same program or based on some program profiles but uses the knowledge gathered from other programs knowledge experience. Also we combined static and a dynamic neural branch predictor in order to investigate how much influences the static predictor the dynamic one.
High accuracy branch some different branch prediction schemes. As an example, the recent architecture. Wide-issue computer architectures rely on microprocessor Alpha uses a large hybrid predictable control flow and failure; to correctly predict predictor [3]. This paper investigates neural static branch a branch involves delays in evacuating the instructions prediction as proposed in [1] but it goes further and links from the wrong path of execution entered into the it with a dynamic neural branch prediction as stated in pipeline structures [7,8].
Statistically was proven that [5,8]. Current wide-issue architectures can execute four or more independent instructions per 2. Static Branch Prediction cycle so a branch instruction is likely to be executed every two cycles or less. This means that branch Good static branch predictions are invaluable prediction is crucial for processor performance.
So in the information for compiler optimisation or performance example of the Alpha processor, about 12 estimation. Typically there are two general approaches instructions may have to be flushed on a misprediction. Many and program-based predictions [8]. Profile-based approaches have been proposed to branch prediction, predictions use program profiles to determine the certain some of which mainly involve hardware dynamic path executed frequency.
This can be extremely branch prediction while others involve software static successful in reducing the number of instructions branch prediction.
Software methods usually cooperate executed between mispredicted branches but additional with hardware methods. Instead, a body of programs structure and to avoid programmer supplementary work.
Every branch of those knowledge that can be encoded in the architecture. Other programs is processed and some static information is techniques rely on applying heuristics based on less automatically extracted. The programs are then executed program structure in an effort to predict branch behavior. Now we have accumulated a heuristics.
This body behavior. However that are mapped using a neural network to the probability the prediction process is yet not complete. The static that the branch will be taken. In this way the predictor prediction is not linked with real time execution unless does not predict a program behavior based on previous there is no connection with dynamic prediction likely execution of the same program but uses the knowledge bits.
Of course, compilers or integrated schedulers may gathered from other programs. So we further generate such bits and use them as inputs into a dynamic predictor.
Useful Information for Program-based The only program elements that we are interested in Branch Prediction are the conditional branches. Other kinds of branches are quite trivial from the direction prediction point of view. This technique relies on the statistical facts conditional branch in the program a set of static useful that backward branches are usually loop branches, and as features are recorded Table 1.
Some of these features such are likely to be taken. While simple, BTFNT is also are properties of the branch instruction itself the branch quite successful. Using this technique we obtained in our opcode, branch direction, etc. We mention that characteristics no. Table 1. Type The branch type from the successor basic block 7 Succ. Loopheader The successor basic block is a loop header 8 Succ. Backedge The edge getting to the successor is a back edge 9 Succ.
Exitedge The edge getting to the successor is a loop exit edge 10 Suc. Call The successor basic block contains a procedure call 11 Succ. Store The successor basic block contains at least one store instruction 12 Succ.
Load The successor basic block contains at least one load instruction Features of the not taken successor of the branch As features 6 to 12 4.
The Statically Neural Predictor as input into a dynamic branch predictor besides other inputs. Also the static prediction will be used as an Our goal into this work is to predict the branch important information in the static scheduler as an probability for a particular branch from its associated example, for the well-known trace scheduling static features.
The prediction rate obtained is then used optimization technique. The static features of a branch are mapped to a the dynamic predictor. This time the input vector probability that the branch will be taken. A simple way consists of binary representation of the branch address to do this is using a feed-forward neural network.
The concatenated with the transformed prediction obtained neural network uses as input a numerical vector and from the static predictor the static prediction obtained maps it to a probability. Similarly, the same type of feed-forward neural network, but another input vector, is used to predict into Figure 1. A general picture of the two neural predictors working together Before this ensemble of predictors static and outputs a value known as its activity.
The connection pattern that So, a set of programs benchmarks is used to train the links the units separates one type of neural network from static neural network. The static features — automatically another. Basically there are two types of neural nets: extracted - for every conditional branch belonging to feed-forward nets which have no loops and recurrent these programs are mapped using the neural network to nets in which loops occurs because of feedback the probability that the branch will be taken.
The output connections. Both neural networks used during this probability itself is obtained after running those work falls into the first category, being thus feed- programs and recording statistical information about forward nets.
The process is from input to output.
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