Chip Multiprocessors (CMPs) allow different applications to share LLC (Last Level Cache). Since each application has different cache capacity demand, LLC capacity should be partitioned in accordance with the demands. Existing partitioning algorithms estimate the capacity demand of each core by stack processing considering the LRU (Least Recently Used) replacement policy only. However, anti-thrashing replacement algorithms like BIP (Binary Insertion Policy) and BIP-Bypass emerged to overcome the thrashing problem of LRU replacement policy in a working set greater than the available cache size. Since existing stack processing cannot estimate the capacity demand with anti-thrashing replacement policy, partitioning algorithms also cannot partition cache space with anti-thrashing replacement policy. In this letter, we prove that BIP replacement policy is not feasible to stack processing but BIP-bypass is. We modify stack processing to accommodate BIP-Bypass. In addition, we propose the pipelined hardware of modified stack processing. With this hardware, we can get the success function of the various capacities with anti-thrashing replacement policy and assess the cache capacity of shared cache adequate to each core in real time.

We achieve concurrent access to WiFi and WiMAX networks on a mobile terminal equipped with a common RF subsystem by providing time-interleaved RF access control schemes to both of the MAC layers. We propose cooperative and competitive sharing schemes, neither of which requires any modification to other network components. We implement our schemes on a WiFi/WiMAX dual-mode SoC platform. Experimental results show that these schemes work and have affordable overheads.

This letter presents two methods of modeling phoneme durations. One is the context-independent phoneme duration modeling in which duration parameters are stored in each phoneme. The other is the context-dependent duration modeling in which duration parameters are stored in each state shared by context-dependent phonemes. The phoneme duration model is compared with a without-duration model and a state duration model. Experiments are performed on a database collected over the telephone network. Experimental results show that duration information rejects out-of-task (OOT) words well and that the context-dependent duration model yields the best performance among the tested models.