Matrix Transform Imager Architecture for On-Chip Low-Power Image Processing

Camera-on-a-chip systems have tried to include carefully chosen signal processing units for better functionality, performance and also to broaden the applications they can be used for. Image processing sensors have been possible due advances in CMOS active pixel sensors (APS) and neuromorphic focal plane imagers. Some of the advantages of these systems are compact size, high speed and parallelism, low power dissipation, and dense system integration. One can envision using these chips for portable and inexpensive video cameras on hand-held devices like personal digital assistants (PDA) or cell-phones In neuromorphic modeling of the retina it would be very nice to have processing capabilities at the focal plane while retaining the density of typical APS imager designs. Unfortunately, these two goals have been mostly incompatible. We introduce our MAtrix Transform Imager Architecture (MATIA) that uses analog floating--gate devices to make it possible to have computational imagers with high pixel densities. The core imager performs computations at the pixel plane, but still has a fill-factor of 46 percent - comparable to the high fill-factors of APS imagers. The processing is performed continuously on the image via programmable matrix operations that can operate on the entire image or blocks within the image. The resulting data-flow architecture can directly perform all kinds of block matrix image transforms. Since the imager operates in the subthreshold region and thus has low power consumption, this architecture can be used as a low-power front end for any system that utilizes these computations. Various compression algorithms (e.g. JPEG), that use block matrix transforms, can be implemented using this architecture. Since MATIA can be used for gradient computations, cheap image tracking devices can be implemented using this architecture. Other applications of this architecture can range from stand-alone universal transform imager systems to systems that can compute stereoscopic depth.Ph.D.Committee Chair: Hasler, Paul; Committee Member: David Anderson; Committee Member: DeWeerth, Steve; Committee Member: Jackson, Joel; Committee Member: Smith, Mar

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

Ultra-Low Power IoT Smart Visual Sensing Devices for Always-ON Applications

This work presents the design of a Smart Ultra-Low Power visual sensor architecture that couples together an ultra-low power event-based image sensor with a parallel and power-optimized digital architecture for data processing. By means of mixed-signal circuits, the imager generates a stream of address events after the extraction and binarization of spatial gradients. When targeting monitoring applications, the sensing and processing energy costs can be reduced by two orders of magnitude thanks to either the mixed-signal imaging technology, the event-based data compression and the use of event-driven computing approaches. From a system-level point of view, a context-aware power management scheme is enabled by means of a power-optimized sensor peripheral block, that requests the processor activation only when a relevant information is detected within the focal plane of the imager. When targeting a smart visual node for triggering purpose, the event-driven approach brings a 10x power reduction with respect to other presented visual systems, while leading to comparable results in terms of detection accuracy. To further enhance the recognition capabilities of the smart camera system, this work introduces the concept of event-based binarized neural networks. By coupling together the theory of binarized neural networks and focal-plane processing, a 17.8% energy reduction is demonstrated on a real-world data classification with a performance drop of 3% with respect to a baseline system featuring commercial visual sensors and a Binary Neural Network engine. Moreover, if coupling the BNN engine with the event-driven triggering detection flow, the average power consumption can be as low as the sleep power of 0.3mW in case of infrequent events, which is 8x lower than a smart camera system featuring a commercial RGB imager

CMOS IMAGE SENSORS FOR LAB-ON-A-CHIP MICROSYSTEM DESIGN

The work described herein serves as a foundation for the development of CMOS imaging in lab-on-a-chip microsystems. Lab-on-a-chip (LOC) systems attempt to emulate the functionality of a cell biology lab by incorporating multiple sensing modalidites into a single microscale system. LOC are applicable to drug development, implantable sensors, cell-based bio-chemical detectors and radiation detectors. The common theme across these systems is achieving performance under severe resource constraints including noise, bandwidth, power and size. The contributions of this work are in the areas of two core lab-on-a-chip imaging functions: object detection and optical measurements

Low-power CMOS digital-pixel Imagers for high-speed uncooled PbSe IR applications

This PhD dissertation describes the research and development of a new low-cost medium wavelength infrared MWIR monolithic imager technology for high-speed uncooled industrial applications. It takes the baton on the latest technological advances in the field of vapour phase deposition (VPD) PbSe-based medium wavelength IR (MWIR) detection accomplished by the industrial partner NIT S.L., adding fundamental knowledge on the investigation of novel VLSI analog and mixed-signal design techniques at circuit and system levels for the development of the readout integrated device attached to the detector. The work supports on the hypothesis that, by the use of the preceding design techniques, current standard inexpensive CMOS technologies fulfill all operational requirements of the VPD PbSe detector in terms of connectivity, reliability, functionality and scalability to integrate the device. The resulting monolithic PbSe-CMOS camera must consume very low power, operate at kHz frequencies, exhibit good uniformity and fit the CMOS read-out active pixels in the compact pitch of the focal plane, all while addressing the particular characteristics of the MWIR detector: high dark-to-signal ratios, large input parasitic capacitance values and remarkable mismatching in PbSe integration. In order to achieve these demands, this thesis proposes null inter-pixel crosstalk vision sensor architectures based on a digital-only focal plane array (FPA) of configurable pixel sensors. Each digital pixel sensor (DPS) cell is equipped with fast communication modules, self-biasing, offset cancellation, analog-to-digital converter (ADC) and fixed pattern noise (FPN) correction. In-pixel power consumption is minimized by the use of comprehensive MOSFET subthreshold operation. The main aim is to potentiate the integration of PbSe-based infra-red (IR)-image sensing technologies so as to widen its use, not only in distinct scenarios, but also at different stages of PbSe-CMOS integration maturity. For this purpose, we posit to investigate a comprehensive set of functional blocks distributed in two parallel approaches: • Frame-based “Smart” MWIR imaging based on new DPS circuit topologies with gain and offset FPN correction capabilities. This research line exploits the detector pitch to offer fully-digital programmability at pixel level and complete functionality with input parasitic capacitance compensation and internal frame memory. • Frame-free “Compact”-pitch MWIR vision based on a novel DPS lossless analog integrator and configurable temporal difference, combined with asynchronous communication protocols inside the focal plane. This strategy is conceived to allow extensive pitch compaction and readout speed increase by the suppression of in-pixel digital filtering, and the use of dynamic bandwidth allocation in each pixel of the FPA. In order make the electrical validation of first prototypes independent of the expensive PbSe deposition processes at wafer level, investigation is extended as well to the development of affordable sensor emulation strategies and integrated test platforms specifically oriented to image read-out integrated circuits. DPS cells, imagers and test chips have been fabricated and characterized in standard 0.15μm 1P6M, 0.35μm 2P4M and 2.5μm 2P1M CMOS technologies, all as part of research projects with industrial partnership. The research has led to the first high-speed uncooled frame-based IR quantum imager monolithically fabricated in a standard VLSI CMOS technology, and has given rise to the Tachyon series [1], a new line of commercial IR cameras used in real-time industrial, environmental and transportation control systems. The frame-free architectures investigated in this work represent a firm step forward to push further pixel pitch and system bandwidth up to the limits imposed by the evolving PbSe detector in future generations of the device.La present tesi doctoral descriu la recerca i el desenvolupament d'una nova tecnologia monolítica d'imatgeria infraroja de longitud d'ona mitja (MWIR), no refrigerada i de baix cost, per a usos industrials d'alta velocitat. El treball pren el relleu dels últims avenços assolits pel soci industrial NIT S.L. en el camp dels detectors MWIR de PbSe depositats en fase vapor (VPD), afegint-hi coneixement fonamental en la investigació de noves tècniques de disseny de circuits VLSI analògics i mixtes pel desenvolupament del dispositiu integrat de lectura unit al detector pixelat. Es parteix de la hipòtesi que, mitjançant l'ús de les esmentades tècniques de disseny, les tecnologies CMOS estàndard satisfan tots els requeriments operacionals del detector VPD PbSe respecte a connectivitat, fiabilitat, funcionalitat i escalabilitat per integrar de forma econòmica el dispositiu. La càmera PbSe-CMOS resultant ha de consumir molt baixa potència, operar a freqüències de kHz, exhibir bona uniformitat, i encabir els píxels actius CMOS de lectura en el pitch compacte del pla focal de la imatge, tot atenent a les particulars característiques del detector: altes relacions de corrent d'obscuritat a senyal, elevats valors de capacitat paràsita a l'entrada i dispersions importants en el procés de fabricació. Amb la finalitat de complir amb els requisits previs, es proposen arquitectures de sensors de visió de molt baix acoblament interpíxel basades en l'ús d'una matriu de pla focal (FPA) de píxels actius exclusivament digitals. Cada píxel sensor digital (DPS) està equipat amb mòduls de comunicació d'alta velocitat, autopolarització, cancel·lació de l'offset, conversió analògica-digital (ADC) i correcció del soroll de patró fixe (FPN). El consum en cada cel·la es minimitza fent un ús exhaustiu del MOSFET operant en subllindar. L'objectiu últim és potenciar la integració de les tecnologies de sensat d'imatge infraroja (IR) basades en PbSe per expandir-ne el seu ús, no només a diferents escenaris, sinó també en diferents estadis de maduresa de la integració PbSe-CMOS. En aquest sentit, es proposa investigar un conjunt complet de blocs funcionals distribuïts en dos enfocs paral·lels: - Dispositius d'imatgeria MWIR "Smart" basats en frames utilitzant noves topologies de circuit DPS amb correcció de l'FPN en guany i offset. Aquesta línia de recerca exprimeix el pitch del detector per oferir una programabilitat completament digital a nivell de píxel i plena funcionalitat amb compensació de la capacitat paràsita d'entrada i memòria interna de fotograma. - Dispositius de visió MWIR "Compact"-pitch "frame-free" en base a un novedós esquema d'integració analògica en el DPS i diferenciació temporal configurable, combinats amb protocols de comunicació asíncrons dins del pla focal. Aquesta estratègia es concep per permetre una alta compactació del pitch i un increment de la velocitat de lectura, mitjançant la supressió del filtrat digital intern i l'assignació dinàmica de l'ample de banda a cada píxel de l'FPA. Per tal d'independitzar la validació elèctrica dels primers prototips respecte a costosos processos de deposició del PbSe sensor a nivell d'oblia, la recerca s'amplia també al desenvolupament de noves estratègies d'emulació del detector d'IR i plataformes de test integrades especialment orientades a circuits integrats de lectura d'imatge. Cel·les DPS, dispositius d'imatge i xips de test s'han fabricat i caracteritzat, respectivament, en tecnologies CMOS estàndard 0.15 micres 1P6M, 0.35 micres 2P4M i 2.5 micres 2P1M, tots dins el marc de projectes de recerca amb socis industrials. Aquest treball ha conduït a la fabricació del primer dispositiu quàntic d'imatgeria IR d'alta velocitat, no refrigerat, basat en frames, i monolíticament fabricat en tecnologia VLSI CMOS estàndard, i ha donat lloc a Tachyon, una nova línia de càmeres IR comercials emprades en sistemes de control industrial, mediambiental i de transport en temps real.Postprint (published version

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

An Optoelectronic Stimulator for Retinal Prosthesis

Retinal prostheses require the presence of viable population of cells in the inner retina. Evaluations of retina with Age-Related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP) have shown a large number of cells remain in the inner retina compared with the outer retina. Therefore, vision loss caused by AMD and RP is potentially treatable with retinal prostheses. Photostimulation based retinal prostheses have shown many advantages compared with retinal implants. In contrary to electrode based stimulation, light does not require mechanical contact. Therefore, the system can be completely external and not does have the power and degradation problems of implanted devices. In addition, the stimulating point is flexible and does not require a prior decision on the stimulation location. Furthermore, a beam of light can be projected on tissue with both temporal and spatial precision. This thesis aims at fi nding a feasible solution to such a system. Firstly, a prototype of an optoelectronic stimulator was proposed and implemented by using the Xilinx Virtex-4 FPGA evaluation board. The platform was used to demonstrate the possibility of photostimulation of the photosensitized neurons. Meanwhile, with the aim of developing a portable retinal prosthesis, a system on chip (SoC) architecture was proposed and a wide tuning range sinusoidal voltage-controlled oscillator (VCO) which is the pivotal component of the system was designed. The VCO is based on a new designed Complementary Metal Oxide Semiconductor (CMOS) Operational Transconductance Ampli er (OTA) which achieves a good linearity over a wide tuning range. Both the OTA and the VCO were fabricated in the AMS 0.35 µm CMOS process. Finally a 9X9 CMOS image sensor with spiking pixels was designed. Each pixel acts as an independent oscillator whose frequency is controlled by the incident light intensity. The sensor was fabricated in the AMS 0.35 µm CMOS Opto Process. Experimental validation and measured results are provided

Smart cmos image sensor for 3d measurement

3D measurements are concerned with extracting visual information from the geometry of visible surfaces and interpreting the 3D coordinate data thus obtained, to detect or track the position or reconstruct the profile of an object, often in real time. These systems necessitate image sensors with high accuracy of position estimation and high frame rate of data processing for handling large volumes of data. A standard imager cannot address the requirements of fast image acquisition and processing, which are the two figures of merit for 3D measurements. Hence, dedicated VLSI imager architectures are indispensable for designing these high performance sensors. CMOS imaging technology provides potential to integrate image processing algorithms on the focal plane of the device, resulting in smart image sensors, capable of achieving better processing features in handling massive image data. The objective of this thesis is to present a new architecture of smart CMOS image sensor for real time 3D measurement using the sheet-beam projection methods based on active triangulation. Proposing the vision sensor as an ensemble of linear sensor arrays, all working in parallel and processing the entire image in slices, the complexity of the image-processing task shifts from O (N 2 ) to O (N). Inherent also in the design is the high level of parallelism to achieve massive parallel processing at high frame rate, required in 3D computation problems. This work demonstrates a prototype of the smart linear sensor incorporating full testability features to test and debug both at device and system levels. The salient features of this work are the asynchronous position to pulse stream conversion, multiple images binarization, high parallelism and modular architecture resulting in frame rate and sub-pixel resolution suitable for real time 3D measurements

Biologically-Inspired Low-Light Vision Systems for Micro-Air Vehicle Applications

Various insect species such as the Megalopta genalis are able to visually stabilize and navigate at light levels in which individual photo-receptors may receive fewer than ten photons per second. They do so in cluttered forest environments with astonishing success while relying heavily on optic flow estimation. Such capabilities are nowhere near being met with current technology, in large part due to limitations of low-light vision systems. This dissertation presents a body of work that enhances the capabilities of visual sensing in photon-limited environments with an emphasis on low-light optic flow detection. We discuss the design and characterization of two optical sensors fabricated using complementary metal-oxide-semiconductor (CMOS) very large scale integration (VLSI) technology. The first is a frame-based, low-light, photon-counting camera module with which we demonstrate 1-D non-directional optic flow detection with fewer than 100 photons/pixel/frame. The second utilizes adaptive analog circuits to improve room-temperature short-wave infrared sensing capabilities. This work demonstrates a reduction in dark current of nearly two orders of magnitude and an improvement in signal-to-noise ratio of nearly 40dB when compared to similar, non-adaptive circuits. This dissertation also presents a novel simulation-based framework that enables benchmarking of optic flow algorithms in photon-limited environments. Using this framework we compare the performance of traditional optic flow processing algorithms to biologically-inspired algorithms thought to be used by flying insects such as the Megalopta genalis. This work serves to provide an understanding of what may be ultimately possible with optic flow sensors in low-light environments and informs the design of future low-light optic flow hardware