Integrated Circuit Design
Research in the Microelectronics Design Lab focuses on VLSI circuits and systems, especially novel computer architectures, analogue and mixed-mode signal processing, brain-inspired systems and chip design for biomedical electronics. The lab has recently moved to a new accommodation, and is well equipped, with industry-standard integrated circuit design tools and test equipment. Below is a brief overview of major research areas, further information, and the list of current projects can be found following this link.
Vision chips are microelectronic devices, which combine image sensing and processing on a single silicon die. In a way somewhat resembling the vertebrate retina, these semiconductor chips perform preliminary image processing directly on the sensory plane. They can be used for computer vision applications in areas such as autonomous vehicle guidance, robotics, industrial inspection or surveillance. We investigate the design of vision chips, using CMOS technologies. Integrating a processing element (PE) within each pixel of the image sensor array results in thousands of processors working concurrently, which enables the processing speeds of billions of operations per second to be easily achieved, at very low power consumption.
Massively parallel processor arrays offer high computing performance and are ideally suited to perform pixel-parallel image processing tasks. Our research focuses on computer architectures based on SIMD processor arrays and asynchronous cellular automata.We have developed several approaches, e.g. SCAMP is a mixed-mode pixel-per-processor SIMD array while ASPA is a cellular processor array which works in asynchronous/synchronous mode running wave-propagating algorithms. We are also developing systems, software tools, and image processing algorithms for cellular processor arrays and vision chips.
Brain-inspired VLSI circuits may one day replace conventional microprocessors as more robust and intelligent systems. They will be also used as controllers for autonomous robots, and in prosthetic devices. We research digital FPGA based, and analogue full-custom VLSI based acceleration engines for neural computation. Currently we participate in two collaborative projects, REVERB and COLAMN, supported under the EPSRC Novel Computation Initiative, where we work with neuroscientists, psychologists and computer scientists, investigating computation in biological neural networks and designing neuromorphic chips and systems implementing natural computation in hardware.
Analogue signal processing circuits can offer higher efficiency (in terms of performance, silicon area and power dissipation) than their digital counterparts. We investigate algorithmically programmable general-purpose analogue microprocessors build using switched-current circuit techniques, analogue implementations of neural networks, and signal processing circuits for biomedical applications, such as high-frequency medical ultrasound systems.