We have proposed a speculative selection function for adaptive routing, which uses idle cycles of the network physical links to exchange network information between nodes, thus helps to decide the best selection. Previous study on the mesh network showed that SSR gives message selection flexibility that improves network performance by balancing the network traffic in both global and local scopes. This paper evaluates the speculative selection function on 2D torus network with simulation. The simulation compares the network throughput and latency with various traffic patterns. The visualization graphs show how the speculative selection eliminates hotspots and disperses traffic in the global scope. The simulation results demonstrate that by using speculative selection, the network performance is increased by around 7%. Compared to the mesh network, the torus's version has smaller gain due to the high performance nature of the torus network.

In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.

The security hash algorithm 512 (SHA-512), which is used to verify the integrity of a message, involves computational iterations on data. The huge computation delay generated in such iterations limits the entire throughput of the system and makes it difficult to pipeline the computation. We describe a way to pipeline the computation using fine-grained pipelining with balanced critical paths. In this method, one critical path is broken into two stages by using data forwarding. The other critical path is broken into three stages by using computation postponement. The resulting critical paths all have two adder-layers with some data movements, and thus are balanced. In addition, the method also allows register reduction. Also, the similarity in SHA-384 and SHA-512 are used for a multi-mode design, which can generate a message digest for both versions with the same throughput, but with only a small increase in hardware size. Experimental results show that our implementation achieved not only the best area performance rate (throughput divided by area), but also a higher throughput than almost all related work.