Three-dimensional (3-D) instrumentation using an image sequence is a promising instrumentation method for intelligent systems in which accurate 3-D information is required. However, real-time instrumentation is difficult since much computation time and a large memory bandwidth are required. In this paper, a 3-D instrumentation VLSI processor with a concurrent memory-access scheme is proposed. To reduce the access time, frequently used data are stored in a cache register array and are concurrently transferred to processing elements using simple interconnections to the 8-nearest neighbor registers. Based on a row and column memory access pattern, we propose a diagonally interleaved frame memory by which pixel values of a row and column are stored across memory modules. Based on the concurrent memory-access scheme, a 40 GOPS vprocessor is designed and the delay time for the instrumentation is estimated to be 42 ms for a 256256 images.

Real-time collision detection is one of the most important intelligent processings in robotics. In collision detection, a large storage capasity is usually required to store the 3-dimensional information on the obstacles located in a workspace. Moreover, high-computational power is essential in not only coordinate transformation but also matching operation. In the proposed collision detection VLSI processor, the matching operation is drastically accelerated by using a content-addressable memory (CAM). A new obstacle representation based on a union of rectangular solids is also used to reduce the obstacle memory capacity, so that the collision detection can be performed by only magnitude comparison in parallel. Parallel architecture using several identical processor elements (PEs) is employed to perform the coordinate transformation at high speed, and each PE performs coordinate transformation at high speed based on the COordinate Rotation DIgital Computation (CORDIC) algorithms. When the 16 PEs and 144-kb CAM are used, the performance is evaluated to be 90 ms.

In robot vision system, enormously large computation power is required to perform three-dimensional (3-D) instrumentation and object recognition. However, many kinds of complex and irregular operations are required to make accurate 3-D instrumentation and object recognition in the conventional method for software implementation. In this paper, a VLSI-oriented Model-Based Robot Vision (MBRV) processor is proposed for high-speed and accurate 3-D instrumentation and object recognition. An input image is compared with two-dimensional (2-D) silhouette images which are generated from the 3-D object models by means of perspective projection. Because the MBRV algorithm always gives the candidates for the accurate 3-D instrumentation and object recognition result with simple and regular procedures, it is suitable for the implementation of the VLSI processor. Highly parallel architecture is employed in the VLSI processor to reduce the latency between the image acquisition and the output generation of the 3-D instrumentation and object recognition results. As a result, 3-D instrumentation and object recognition can be performed 10000 times faster than a 28.5 MIPS workstation.

Since carelessness in driving causes a terrible traffic accident, it is an important subject for a vehicle to avoid collision autonomously. Real-time collision detection between a vehicle and obstacles will be a key target for the next-generation car electronics system. In collision detection, a large storage capacity is usually required to store the 3-D information on the obstacles lacated in a workspace. Moreover, high-computational power is essential not only in coordinate transformation but also in matching operation. In the proposed collision detection VLSI processor, the matching operation is drastically accelerated by using a Content-Addressable Memory (CAM) which evaluates the magnitude relationships between an input word and all the stored words in parallel. A new obstacle representation based on a union of rectangular solids is also used to reduce the obstacle memory capacity, so that the collision detection can be parformed only by parallel magnitude comparison. Parallel architecture using several identical processor elements (PEs) is employed to perform the coordinate transformation at high speed based on the COordinate Rotation DIgital Computation (CORDIC) algorithms. The collision detection time becomes 5.2 ms using 20 PEs and five CAMs with a 42-kbit capacity.

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.