This paper proposes parallel VLSI processors for robotics based on multiple processing elements organized around multiple bus interconnection networks. The advantages of multiple bus interconnection networks are generality, simplicity of implementation and capability of parallel communications between processing elements, therefore it is considered to be suitable for parallel VLSI systems. We also propose the optimal scheduling formulated in an integer programming problem to minimize the delay time of the parallel VLSI processors.

The performance of processing elements can be improved by the progress of VLSI circuit technology, while the communication overhead can not be negligible in parallel processing system. This paper presents a unified scheduling that allocates tasks having different task processing times in multiple processing elements. The objective function is formulated to measure communication time between processing elements. By employing constraint conditions, the scheduling efficiently generates an optimal solution using an integer programming so that minimum communication time can be achieved. We also propose a VLSI processor for robotics whose latency is very small. In the VLSI processor, the data transfer between two processing elements can be done very quickly, so that the communication cycle time is greatly reduced.

In intelligent integrated systems such as robotics for autonomous work, it is essential to respond to the change of the environment very quickly. Therefore, the development of special-purpose VLSI processors for intelligent integrated systems with small latency becomes an very important subject. In this paper, we present a scheduling algorithm for high-level synthesis. The input to the scheduler is a behavioral description which is viewed as a data flow graph (DFG). The scheduler minimizes the latency, which is the delay of the critical path in the DFG, and minimizes the number of functional units and buses by improving the utilization rates. By using an integer linear programming, the scheduler optimally assigns nodes and arcs in the DFG into steps.