This paper studies mapping techniques of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper are static which means the mapping is decided at design time. The mapping techniques take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the many-core architecture. Additionally, the proposed static mapping supports dynamic application switching, which means the applications mapped onto the same cores are switched to each other at runtime. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experimental results show the effectiveness of the proposed techniques.

Behavioral synthesis, which automatically synthesizes an RTL circuit from a sequential program, is one of promising technologies to improve the design productivity. This paper proposes a function call optimization method in behavioral synthesis from large sequential programs with a number of functions. We formulate the optimization problem using integer linear programming. Our experimental results show the reduction in the circuit area by up to 44.6%, compared with a traditional method.

Microfluidic biochips, also referred to “lab-on-a-chip,” have been recently proposed to integrate all the necessary functions for biochemical analyses. This technology starts a new era of biology science, where a combination of electronic and biology is first introduced. There are several types of microfluidic biochips; among them there has been a great interest in flow-based microfluidic biochips, in which the flows of liquid is manipulated using integrated microvalves. By combining several microvalves, more complex resource units such as micropumps, switches and mixers can be built. For efficient execution, the flows of liquid routes in microfluidic biochips need to be scheduled under some resource constraints and routing constraints. The execution time of a biochemical application depends strongly on the binding and scheduling result. The most previously developed binding and scheduling algorithm is based on heuristics, and there has been no method to obtain optimal results. Considering the above, we propose an optimal method by casting the problem to a clique problem. Moreover, this paper also presents some heuristic techniques for computational time reduction. Experiments demonstrate that the proposed method is able to reduce the execution time of biochemical applications by more than 15% compared with the previous approach. Moreover, the proposed heuristic method is able to produce the results at no or little cost of optimality, in significantly shorter time than the optimal method.

Nowadays, fault tolerance has been playing a progressively important role in covering increasing soft/hard error rates in electronic devices that accompany the advances of process technologies. Research shows that wear-out faults have a gradual onset, starting with a timing fault and then eventually leading to a permanent fault. Error detection is thus a required function to maintain execution correctness. Currently, however, many highly dependable methods to cover permanent faults are commonly over-designed by using very frequent checking, due to lack of awareness of the fault possibility in circuits used for the pending executions. In this research, to address the over-checking problem, we introduce a metric for permanent defects, as operation defective probability (ODP), to quantitatively instruct the check operations being placed only at critical positions. By using this selective checking approach, we can achieve a near-100% dependability by having about 53% less check operations, as compared to the ideal reliable method, which performs exhaustive checks to guarantee a zero-error propagation. By this means, we are able to reduce 21.7% power consumption by avoiding the non-critical checking inside the over-designed approach.

This paper studies the static mapping of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the many-core architecture. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experiments show the effectiveness of the proposed techniques.

A novel method to efficiently synthesize hardware from a large behavioral description in behavioral synthesis is proposed. For a program with functions executable in parallel, this proposed method determines a behavioral partitioning which simultaneously minimizes the overall datapath area and the complexity of the controller while maximizing performance of a synthesized circuit by fully exploiting function-level parallelism of a behavioral description. This method is formulated as an integer programming problem. Experimental results demonstrate that this method leads to a shift of the explorable design space so that superior solutions which could not be explored by earlier work are included, showing the effectiveness of our proposed method.

This paper proposes a behavioral level partitioning method for efficient behavioral synthesis from a large sequential program consisting of a set of functions. Our method optimally determines functions to be inlined into the main module and the other functions to be synthesized into sub modules in such a way that the overall datapath is minimized while the complexity of individual modules is lower than a certain level. The partitioning problem is formulated as an integer programming problem. Experimental results show the effectiveness of the proposed method.

Approximate computing is considered as a promising approach to design of power- or area-efficient digital circuits. This paper proposes a systematic methodology for design and worst-case accuracy analysis of approximate array multipliers. Our methodology systematically designs a series of approximate array multipliers with different area, delay, power and accuracy characteristics so that an LSI designer can select the one which best fits to the requirements of her/his applications. Our experiments explore the trade-offs among area, delay, power and accuracy of the approximate multipliers.