A 10-gigabit Ethernet passive optical network (10G-EPON) is promising for the next generation of access networks. A protocol processor for 10G-EPON needs to not only achieve 10-Gbps throughput but also to have protocol extendibility for various potential services. However, the conventional protocol processor does not have the ability to install additional protocols after chip fabrication, due to its hardware-based architecture. This paper presents a software-hardware cooperative protocol processor for 10G-EPON that provides the protocol extendibility. To achieve the software-hardware cooperation, the protocol processor newly employs a software-hardware partitioning technique driven by the timing requirements of 10G-EPON and a software-hardware interface circuit with event FIFO to absorb performance difference between software and hardware. The fabricated chip with this protocol processor properly works cooperatively and is able to accept newly standardized protocols. This protocol processor enables network operators to install additional service protocols adaptively for their own services.

In this paper, we propose a hardware solution for overcoming the problem of high computational demands in a nearest neighbor (NN) based multi-prototype learning system. The multiple prototypes are obtained by a high-speed K-means clustering algorithm utilizing a concept of software-hardware cooperation that takes advantage of the flexibility of the software and the efficiency of the hardware. The one nearest neighbor (1-NN) classifier is used to recognize an object by searching for the nearest Euclidean distance among the prototypes. The major deficiency in conventional implementations for both K-means and 1-NN is the high computational demand of the nearest neighbor searching. This deficiency is resolved by an FPGA-implemented coprocessor that is a VLSI circuit for searching the nearest Euclidean distance. The coprocessor requires 12.9% logic elements and 58% block memory bits of an Altera Stratix III E110 FPGA device. The hardware communicates with the software by a PCI Express (4) local-bus-compatible interface. We benchmark our learning system against the popular case of handwritten digit recognition in which abundant previous works for comparison are available. In the case of the MNIST database, we could attain the most efficient accuracy rate of 97.91% with 930 prototypes, the learning speed of 1.310-4 s/sample and the classification speed of 3.9410-8 s/character.