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.

In the applications of the fast Fourier transform (FFT) to real-world computation such as robot vision, high-speed processing with small latency is an important issue. In this paper, we propose a linear array processor for the minimum-latency FFT computation. The processor is constructed by identical butterfly elements (BE's). The key concept to minimize the latency is that each BE generates its output data immediately after its input data become available, with 100% utilization of its arithmetic unit. We also introduce the real-valued FFT to perform the complex-valued FFT. We utilize a double linear array structure so that the parallel processing can be realized without communication between the linear arrays. As a result, the hardware amount of a single BE is reduced to half that of conventional designs. The latency of the proposed FFT processor is greatly reduced in comparison with conventional linear array FFT processors.