A multi-core event-driven parallel processor array design is presented. Using relatively simple 8-bit processing cores and a 2D mesh network topology, the architecture focuses on reducing the area occupation of a single processor core. A large number of these processor cores can be implemented on a single integrated chip to create a MIMD architecture capable of providing a powerful processing performance. Each processor core is an event-driven processor which can enter an idle mode when no data is changing locally. An 8 × 8 prototype processor array is implemented in a 65 nm CMOS process in 1,875 μm × 1,875 μm. This processor array is capable of performing 5.12 GOPS operating at 80 MHz with an average power consumption of 75.4 mW.

A versatile simulation system is presented, for observing the effects of different algorithmic and mechanical approaches to physical object manipulation via "smart" surfaces -physical machines which use a distributed array of actuators algorithmically controlled to manipulate objects placed upon them. The system presented is a graphical 3D physics sandbox simulator that allows the modeller to include real-world and hardware related constraints such as inter-object collisions and trajectories, mechanical properties, friction, and sensor/actuator reliability, while conducting experiments in an interactive, repeatable and controlled environment.

Massively parallel processor arrays have been shown to be an effective and suitable choice for image processing tasks [1]. More recently, some of the state of the art processor arrays have been used for real-time machine vision tasks such as intelligent transport system applications [2] or video processing on mobile applications [3] providing a much more powerful solution than a conventional processor. A number of Single Instruction Multiple Data (SIMD) processor arrays have been implemented on FPGAs [4]-[6], which are particularly suited to implementing such processor architectures because of their similarities of both being arrays of fine grained logic elements. In this work, we propose an FPGA implementation of a processor array where the processing elements (PEs) are as small as possible, while providing local memory sufficient for processing greyscale images. The PE is then replicated to form an array. A 32 × 32 PE array is implemented on a Xilinx Virtex 5 XC5VLX50 FPGA using the four-neighbour connectivity with the possibility to scale up using a larger FPGA. The processor array operates at a frequency of 96 MHz and executes a peak of 98.3 giga operations per second (GOPS) (bit-serial operations). A binary edge detection algorithm is performed in 52.08 ns. Uploading and downloading a binary image in a 32 × 32 array takes an extra 687.5 ns. Sobel edge detection of an 8-bit greyscale image is performed in 5.33 µs. Uploading and downloading an 8-bit greyscale image in a 32 × 32 array takes 5.36 µs. With larger FPGAs being available in the future, the array sizes comparable to state of the art custom designed ICs can be implemented on these FPGAs.