A micropower vision processor for parallel object positioning and sizing

Accepted versio

A micropower centroiding vision processor

Published versio

Bio-inspired electronics for micropower vision processing

Vision processing is a topic traditionally associated with neurobiology; known to encode, process and interpret visual data most effectively. For example, the human retina; an exquisite sheet of neurobiological wetware, is amongst the most powerful and efficient vision processors known to mankind. With improving integrated technologies, this has generated considerable research interest in the microelectronics community in a quest to develop effective, efficient and robust vision processing hardware with real-time capability. This thesis describes the design of a novel biologically-inspired hybrid analogue/digital vision chip ORASIS1 for centroiding, sizing and counting of enclosed objects. This chip is the first two-dimensional silicon retina capable of centroiding and sizing multiple objects2 in true parallel fashion. Based on a novel distributed architecture, this system achieves ultra-fast and ultra-low power operation in comparison to conventional techniques. Although specifically applied to centroid detection, the generalised architecture in fact presents a new biologically-inspired processing paradigm entitled: distributed asynchronous mixed-signal logic processing. This is applicable to vision and sensory processing applications in general that require processing of large numbers of parallel inputs, normally presenting a computational bottleneck. Apart from the distributed architecture, the specific centroiding algorithm and vision chip other original contributions include: an ultra-low power tunable edge-detection circuit, an adjustable threshold local/global smoothing network and an ON/OFF-adaptive spiking photoreceptor circuit. Finally, a concise yet comprehensive overview of photodiode design methodology is provided for standard CMOS technologies. This aims to form a basic reference from an engineering perspective, bridging together theory with measured results. Furthermore, an approximate photodiode expression is presented, aiming to provide vision chip designers with a basic tool for pre-fabrication calculations

Optimization Techniques for Parallel Programming of Embedded Many-Core Computing Platforms

Nowadays many-core computing platforms are widely adopted as a viable solution to accelerate compute-intensive workloads at different scales, from low-cost devices to HPC nodes. It is well established that heterogeneous platforms including a general-purpose host processor and a parallel programmable accelerator have the potential to dramatically increase the peak performance/Watt of computing architectures. However the adoption of these platforms further complicates application development, whereas it is widely acknowledged that software development is a critical activity for the platform design. The introduction of parallel architectures raises the need for programming paradigms capable of effectively leveraging an increasing number of processors, from two to thousands. In this scenario the study of optimization techniques to program parallel accelerators is paramount for two main objectives: first, improving performance and energy efficiency of the platform, which are key metrics for both embedded and HPC systems; second, enforcing software engineering practices with the aim to guarantee code quality and reduce software costs. This thesis presents a set of techniques that have been studied and designed to achieve these objectives overcoming the current state-of-the-art. As a first contribution, we discuss the use of OpenMP tasking as a general-purpose programming model to support the execution of diverse workloads, and we introduce a set of runtime-level techniques to support fine-grain tasks on high-end many-core accelerators (devices with a power consumption greater than 10W). Then we focus our attention on embedded computer vision (CV), with the aim to show how to achieve best performance by exploiting the characteristics of a specific application domain. To further reduce the power consumption of parallel accelerators beyond the current technological limits, we describe an approach based on the principles of approximate computing, which implies modification to the program semantics and proper hardware support at the architectural level

NASA Tech Briefs, October 2000

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Design of electronic systems for automotive sensor conditioning

This thesis deals with the development of sensor systems for automotive, mainly targeting the exploitation of the new generation of Micro Electro-Mechanical Sensors (MEMS), which achieve a dramatic reduction of area and power consumption but at the same time require more complexity in the sensor conditioning interface. Several issues concerning the development of automotive ASICs are presented, together with an overview of automotive electronics market and its main sensor applications. The state of the art for sensor interfaces design (the generic sensor interface concept), consists in sharing the same electronics among similar sensor applications, thus saving cost and time-to-market but also implementing a sub-optimal system with area and power overheads. A Platform Based Design methodology is proposed to overcome the limitations of generic sensor interfaces, by keeping the platform generality at the highest design layers and pursuing the maximum optimization and performances in the platform customization for a specific sensor. A complete design flow is presented (up to the ASIC implementation for gyro sensor conditioning), together with examples regarding IP development for reuse and low power optimization of third party designs. A further evolution of Platform Based Design has been achieved by means of implementation into silicon of the ISIF (Intelligent Sensor InterFace) platform. ISIF is a highly programmable mixed-signal chip which allows a substantial reduction of design space exploration time, as it can implement in a short time a wide class of sensor conditioning architectures. Thus it lets the designers evaluate directly on silicon the impact of different architectural choices, as well as perform feasibility studies, sensor evaluations and accurate estimation of the resulting dedicated ASIC performances. Several case studies regarding fast prototyping possibilities with ISIF are presented: a magneto-resistive position sensor, a biosensor (which produces pA currents in presence of surface chemical reactions) and two capacitive inertial sensors, a gyro and a low-g YZ accelerometer. The accelerometer interface has also been implemented in miniboards of about 3 cm2 (with ISIF and sensor dies bonded together) and a series of automatic trimming and characterization procedures have been developed in order to evaluate sensor and interface behaviour over the automotive temperature range, providing a valuable feedback for the implementation of a dedicated accelerometer interface

Low Power Circuits for Miniature Sensor Systems.

With the development of VLSI technologies, the sensor systems of all kinds of applications have entered our everyday's life. For specific applications such as medical implants, the form factor of such systems is the crucial concern. In order to minimize of size of the power sources with a given lifetime, the ability to operate the system with low power consumption is the key. An effective way of lowering the active power dissipation is through aggressive voltage scaling. For minimal energy operation, the optimum supply voltage is typical lower than the subthreshold voltage. On the other hand, a sensor system spends most of the time idling while only actively obtaining data in a short period of time. As a result, strong power gating is needed for reducing the leakage power. We discuss the design challenges for several building blocks for the sensor system that have not been gotten much emphasis in term of power consumption. To monitor the period for idle time and to wake up the system periodically, two types of ultra low power timers are proposed. The first one utilizes the gate leakage of a MOS transistor to achieve low temperature dependency and large time constant. The second one implements a program-and-hold technique to compensate for the temperature coefficient of a one-shot oscillator with 150pW of average power. We propose a low power temperature sensor that is suitable for passive RFID transponder. To retrieve the data out of the sensor chip, two passive proximity communication schemes are presented. Capacitive coupling can be used for chips on a stack where the key challenge is misalignment. A alignment detection and microplate reconfiguration method is proposed to solve the problem. We also propose a passive inductive coupling scheme using pulse signaling. Compared to the traditional backscattering technique, the limitations on the quality factor of the inductor and the signal sensitivity of the receiver can be relaxed.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61782/1/yushiang_1.pd

Heterogeneous Architectures For Parallel Acceleration

To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's computer architecture - and at the same time we show methodologies to introduce it with little or no loss in terms of flexibility. In particular, we show that heterogeneity can be employed to tackle the "walls" that impede further development of new computing applications: the utilization wall, i.e. the impossibility to keep all transistors on in deeply integrated chips, and the "data deluge", i.e. the amount of data to be processed that is scaling up much faster than the computing performance and efficiency. We introduce a methodology to improve heterogeneous design exploration of tightly coupled clusters; moreover we propose a fractal heterogeneity architecture that is a parallel accelerator for low-power sensor nodes, and is itself internally heterogeneous thanks to an heterogeneous coprocessor for brain-inspired computing. This platform, which is silicon-proven, can lead to more than 100x improvement in terms of energy efficiency with respect to typical computing nodes used within the same domain, enabling the application of complex algorithms, vastly more performance-hungry than the current state-of-the-art in the ULP computing domain

NASA Tech Briefs, October 1996

Topics covered include: Sensors; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports