This paper presents a DFT controller called as a TCU (Test Control Unit), which considerably improves the efficiency of the instruction-based functional test. Internal program/data buses are completely controllable and observable by the TCU during the test cycle. Diverse test modes of the TCU can increase the test efficiency and also provide complete access to program/data memories for functional test.

This paper proposes a hardware-based parallel pattern matching engine using a memory-based bit-split string matcher architecture. The proposed bit-split string matcher separates the transition table from the state table, so that state transitions towards the initial state are not stored. Therefore, total memory requirements can be minimized.

Scientific computations for diffusion equations and ANNs (Artificial Neural Networks) are data intensive tasks accompanied by heavy memory access; on the other hand, their computational complexities are relatively low. Thus, this type of tasks naturally maps onto SIMD (Single Instruction Multiple Data stream) parallel processing with distributed memory. This paper proposes a high performance acceleration processor of which architecture is optimized for scientific computing using diffusion equations and ANNs. The proposed architecture includes a customized instruction set and specific hardware resources which consist of a control unit (CU), 16 processing units (PUs), and a non-linear function unit (NFU) on chip. They are effectively connected with dedicated ring and global bus structure. Each PU is equipped with an address modifier (AM) and 16-bit 1.5 k-word local memory (LM). The proposed processor can be easily expanded by multi-chip expansion mode to accommodate to a large scale parallel computation. The prototype chip is implemented with FPGA. The total gate count is about 1 million with 530, 432-bit embedded memory cells and it operates at 15 MHz. The functionality and performance of the proposed processor is verified with simulation of oil reservoir problem using diffusion equations and character recognition application using ANNs. The execution times of two applications are compared with software realizations on 1.7 GHz Pentium IV personal computer. Though the proposed processor architecture and the instruction set are optimized for diffusion equations and ANNs, it provides flexibility to program for many other scientific computation algorithms.

This paper describes a channel-adaptive packet scheduler for improved error control performance in a peer-cooperative distributed media streaming system. The proposed packet-scheduling algorithm was designed for the case in which streaming server peers rely on an error-recovery strategy using retransmission and application-layer automatic repeat request rather than error protection using forward error correction. The proposed scheduler can maximize retransmission opportunities and reduce the frame loss rate by using the observed channel status from each server peer. Simulation results show that the proposed algorithm enhances error-recovery performance in distributed multimedia streaming better than other schedulers.

This letter proposes a fast IP address lookup algorithm based on search space reduction. Prefixes are classified into three types according to the nesting relationship and a large forwarding table is partitioned into multiple small trees. As a result, the search space is reduced. The results of analyses and experiments show that the proposed method offers higher lookup and updating speeds along with reduced memory requirements.