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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.
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.
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.
The Branch-and-Bound algorithm [7] does reduce this complexity but is intractable for medium to large values of the number of available features Techniques to transform the raw feature vectors ( feature extraction ) are sometimes used prior to application of the pattern-matching algorithm.
This approach is employed in a variety of areas, including branch prediction in pipelined processors, value prediction for exploiting value locality, prefetching memory and files, and optimistic concurrency control in database systems. [1] [2] [3] Speculative multithreading is a special case of speculative execution.
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).
There are two major flavors of algorithms for Multiple Instance Learning: instance-based and metadata-based, or embedding-based algorithms. The term "instance-based" denotes that the algorithm attempts to find a set of representative instances based on an MI assumption and classify future bags from these representatives.