Search results
Results from the WOW.Com Content Network
A two-level adaptive predictor with globally shared history buffer and pattern history table is called a "gshare" predictor if it xors the global history and branch PC, and "gselect" if it concatenates them. Global branch prediction is used in AMD processors, and in Intel Pentium M, Core, Core 2, and Silvermont-based Atom processors.
The general rule for a two-level adaptive predictor with an n-bit history is that it can predict any repetitive sequence with any period if all n-bit sub-sequences are different.[8] The advantage of the two-level adaptive predictor is that it can quickly learn to predict an arbitrary repetitive pattern.
As predicted branches happen every 10 instructions or so, this can force a substantial drop in fetch bandwidth. Some machines with longer instruction cache latencies would have an even larger loss. To ameliorate the loss, some machines implement branch target prediction: given the address of a branch, they predict the target of that branch.
With predication, all possible branch paths are coded inline, but some instructions execute while others do not. The basic idea is that each instruction is associated with a predicate (the word here used similarly to its usage in predicate logic) and that the instruction will only be executed if the predicate is true.
An adaptive algorithm is an algorithm that changes its behavior at the time it is run, [1] based on information available and on a priori defined reward mechanism (or criterion). Such information could be the story of recently received data, information on the available computational resources, or other run-time acquired (or a priori known ...
In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.
Code-excited linear prediction (CELP) is a linear predictive speech coding algorithm originally proposed by Manfred R. Schroeder and Bishnu S. Atal in 1985. At the time, it provided significantly better quality than existing low bit-rate algorithms, such as residual-excited linear prediction (RELP) and linear predictive coding (LPC) vocoders (e.g., FS-1015).
ML involves the study and construction of algorithms that can learn from and make predictions on data. [3] These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.