Abstract. Conditional branch predictor (CBP) is an essential component in the design of any modern deeply pipelined superscalar microprocessor architecture. In the recent past, many researchers have proposed varieties schemes for the design of the CBPs that claim to offer desired levels of accuracy and speed needed to meet the demand for the architectural design of multicore processors. Amongst various schemes in practice to realize the CBPs, the ones based on neural computing – i.e., artificial neural network (ANN) have been found to outperform other CBPs in terms of accuracy.
Functional link artificial neural network (FLANN) is a single layer ANN with low computational complexity which has been used in versatile fields of application,
…show more content…
The major dificulty encountered due to extensive use of parallelism is the existence of branch instructions in the set of instructions presented to the processor for execution: both conditional and unconditional branch instructions in the pipeline. If the instructions under execution in the pipeline does not bring any change in the control flow of the program, then there is no problem at all. However, when the branch instruction puts the program under execution to undergo a change in the flow of control, the situation becomes a topic of concern as the branch instruction breaks the sequential flow of control, leading to a situation what is called pipeline stall and levying heavy penalties on processing in the form of execution delays, breaks in the program flow and overall performance drop. Changes in the control flow affects the processor performance because many processor cycles must be wasted in ushing the pipeline already loaded with instructions from wrong locations and again reading-in the new set of instructions from right address. It is well known that in a highly parallel computer system, branch instructions can break the smooth flow of instruction fetching, decoding and execution. The consequence of this is in delay, because the instruction issuing must often wait until the actual branch outcome is known. To make things worse, the deeper the pipelining, more is the delay, and thus greater is the performance
ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.
Symmetric multiprocessing treats all processors similarly. I/O can be processed on any processor. The processors interconnect with each other as needed. It allows many processes to be run at once without corrupting performance. Symmetric multiprocessing treats all processors similarly. I/O can be processed on any processor. The processors interconnect with each other as needed. It allows many processes to be run at once without corrupting performance. Three advantages of multiprocessing are: Increased throughput - with more processors, more work can be accomplished in less time; Economy of scale - peripheral devices may be shared amongst multi-processor systems; increased reliability - if one processor crashes, then the others may continue to operate. One disadvantage of a multi-processing system is the added difficulty in operating system and possibly application software. Another limitation of SMP is that as microprocessors are added, the shared bus get overloaded and becomes a performance bottleneck. Symmetric Multiprocessor Master-slave multiprocessor is not reliable as if the master processor fails the whole system goes down.
A multicore CPU has various execution centers on one CPU. Presently, this can mean distinctive things relying upon the precise construction modeling, however it fundamentally implies that a sure subset of the CPU's segments is copied, so that various "centers" can work in parallel on partitioned operations. This is Chip-level Multprocessing (CMP).
In spite of the fact that multiprocessors have numerous favorable position it additionally have some detriment like complex in structure when contrasted with uni-processor framework.
Data buffering is helpful for smoothing out the speed difference between CPU and input/ output devices
6.10) I/O-bound projects have the property of performing just a little measure of computation before performing I/O. Such projects regularly don't use up their whole CPU quantum. Whereas, in case of CPU-bound projects, they utilize their whole quantum without performing any blocking I/O operations. Subsequently, one could greatly improve the situation utilization of the computer’s assets by giving higher priority to I/O-bound projects and permit them to execute in front of the CPU-bound
In contrast, the parallelism is a condition that comes when at least two threads are executing at the same time''. It is possible for two threads to make progress, though not at the same
The following steps are used to design the back propagation neural network algorithm for the proposed research work. The first step is to set the input, output data sets. The second step is to set the number of hidden layer and output activation functions. The third step is to set the training functions and training parameters, finally run the network.
The processor (otherwise known as CPU) is the very soul and performance core of the computer system; it is what allows the operating system and other software applications to-run. Every program demands dedication from the processor to decode commands that are then actionedinside the CPU to make them work.When a program is running, the CPU has to make every command work consistently one after the other. However, modern processors have the power to process commands side by side. This means that the quicker the commands are executed, the quicker the program responds to the user. Central Processing Units (CPUs) play an important role when it comes to maintaining
The processing unit of a single - layer perceptor network can be able to solve the
The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,
Since the invention of the first computer, engineers have been conceptualizing and implementing ways to optimize system performance. The last 25 years have seen a rapid evolution of many of these concepts, particularly cache memory, virtual memory, pipelining, and reduced set instruction computing (RISC). Individual each one of these concepts has helped to increase speed and efficiency thus enhancing overall system performance. Most systems today make use of many, if not all of these concepts. Arguments can be made to support the importance of any one of these concepts over one
As technology advances, the processes that we use to manage that technology become more demanding, creating the need for new software and efficient processors. “The central processing unit or (CPU) is the heart of your computer and is used to run the operating system as well as all the programs.” (Chris Hoffman, CPU Basics: multiple CPU’s, cores and hyper threading explained.) With so much power in a single chip, we have created a powerful piece of technology that can be placed virtually anywhere.
After running a process flow [see Exhibit 2], it becomes apparent that a main bottleneck exists at the
4. Performance Comparison of Dual Core Processors Using Multiprogrammed and Multithreaded Benchmarks ............................................................................................... 31 4.1 Overview ........................................................................................................... 31 4.2 Methodology ..................................................................................................... 31 Multiprogrammed Workload Measurements .................................................... 33 4.3 4.4 Multithreaded Program Behavior ..................................................................... 36 5. 6. Related Work ............................................................................................................ 39 Conclusion ................................................................................................................ 41