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

AER Auditory Filtering and CPG for Robot Control

Address-Event-Representation (AER) is a communication protocol for transferring asynchronous events between VLSI chips, originally developed for bio-inspired processing systems (for example, image processing). The event information in an AER system is transferred using a highspeed digital parallel bus. This paper presents an experiment using AER for sensing, processing and finally actuating a Robot. The AER output of a silicon cochlea is processed by an AER filter implemented on a FPGA to produce rhythmic walking in a humanoid robot (Redbot). We have implemented both the AER rhythm detector and the Central Pattern Generator (CPG) on a Spartan II FPGA which is part of a USB-AER platform developed by some of the authors.Commission of the European Communities IST-2001-34124 (CAVIAR)Comisión Interministerial de Ciencia y Tecnología TIC-2003-08164-C03-0

Matching properties and applications of compatible lateral bipolar transistors (CLBTs).

Hiu Yung Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 104-111).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.iiiList of Figures --- p.ixList of Tables --- p.xiiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation and Objectives --- p.1Chapter 1.2 --- Contributions --- p.3Chapter 1.3 --- Organization of the Thesis --- p.4Chapter 2 --- Devices and Fabrication Processes --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- BJTs --- p.6Chapter 2.2.1 --- Structure and Modeling of BJTs --- p.6Chapter 2.2.2 --- Standard BJT Process and BJT Characteristics --- p.7Chapter 2.3 --- MOSFETs and Complementary MOS (CMOS) --- p.8Chapter 2.3.1 --- Structure and Modeling of MOSFETs --- p.8Chapter 2.3.2 --- Standard n-well CMOS Process and MOSFETs Charac- teristics --- p.11Chapter 2.4 --- BiCMOS Technology --- p.13Chapter 2.5 --- Summary --- p.14Chapter 3 --- Matching Properties --- p.15Chapter 3.1 --- Introduction --- p.15Chapter 3.2 --- Importance of Matched Devices in IC Design --- p.15Chapter 3.2.1 --- What is Matching? --- p.15Chapter 3.2.2 --- Low-power Systems --- p.16Chapter 3.2.3 --- Device Size Downward Scaling --- p.16Chapter 3.2.4 --- Analog Circuits and Analog Computing --- p.17Chapter 3.3 --- Measurement of Mismatch --- p.18Chapter 3.3.1 --- Definitions and Statistics of Mismatch --- p.18Chapter 3.3.2 --- Types of Mismatches --- p.20Chapter 3.3.3 --- Matching Properties of MOSFETs --- p.23Chapter 3.3.4 --- Matching Properties of BJTs and CLBTs --- p.27Chapter 3.4 --- Summary --- p.30Chapter 4 --- CMOS Compatible Lateral Bipolar Transistors (CLBTs) --- p.31Chapter 4.1 --- Introduction --- p.31Chapter 4.2 --- Structure and Operation --- p.32Chapter 4.3 --- DC Model of CLBTs --- p.34Chapter 4.4 --- Residual Gate Effect in Accumulation --- p.35Chapter 4.5 --- Main Characteristics of CLBTs --- p.37Chapter 4.5.1 --- Low Early Voltage --- p.37Chapter 4.5.2 --- Low Lateral Current Gain at High Current Levels --- p.38Chapter 4.5.3 --- Other Issues --- p.39Chapter 4.6 --- Enhanced CLBTs with Cascode Circuit --- p.40Chapter 4.7 --- Applications --- p.41Chapter 4.8 --- Design and Layout of CLBTs --- p.42Chapter 4.9 --- Experimental Results of Single pnp CLBT； nMOSFET and pMOSFET --- p.44Chapter 4.9.1 --- CLBT Gains --- p.46Chapter 4.9.2 --- Gate Voltage Required for Pure Bipolar Action --- p.47Chapter 4.9.3 --- I ´ؤ V and Other Characteristics of Bare pnp CLBTs --- p.49Chapter 4.9.4 --- Transfer Characteristics of a Cascoded pnp CLBT --- p.50Chapter 4.9.5 --- Transfer Characteristics of an nMOSFET --- p.51Chapter 4.9.6 --- Transfer Characteristics of Cascoded and Bare CLBTs Operating as pMOSFETs --- p.52Chapter 4.10 --- Summary --- p.53Chapter 5 --- Experiments on Matching Properties --- p.54Chapter 5.1 --- Introduction --- p.54Chapter 5.2 --- Objectives --- p.55Chapter 5.3 --- Technology --- p.57Chapter 5.4 --- Design of Testing Arrays --- p.57Chapter 5.4.1 --- nMOSFET Array --- p.57Chapter 5.4.2 --- pnp CLBT Array --- p.59Chapter 5.5 --- Design of Input and Output Pads (I/O Pads) --- p.62Chapter 5.6 --- Shift Register --- p.62Chapter 5.7 --- Experimental Equipment --- p.63Chapter 5.8 --- Experimental Setup for Matching Properties Measurements --- p.65Chapter 5.8.1 --- Setup for Measuring the Mismatches of the Devices --- p.65Chapter 5.8.2 --- Testing Procedures --- p.68Chapter 5.8.3 --- Data Analysis --- p.68Chapter 5.9 --- Matching Properties --- p.69Chapter 5.9.1 --- Matching Properties of nMOSFETs --- p.69Chapter 5.9.2 --- Matching Properties of CLBTs --- p.71Chapter 5.9.3 --- Matching Properties of pMOSFETs --- p.73Chapter 5.9.4 --- "Comments on the Matching Properties of CLBT, nMOSFET, and pMOSFET" --- p.76Chapter 5.9.5 --- "Mismatch in CLBT, nMOSFET, and pMOSFET Cur- rent Mirrors" --- p.77Chapter 5.10 --- Summary --- p.79Chapter 6 --- Conclusion --- p.80Chapter A --- Floating Gate Technology --- p.82Chapter A.1 --- Floating Gate --- p.82Chapter A.2 --- Tunnelling --- p.83Chapter A.3 --- Hot Electron Effect --- p.85Chapter A.4 --- Summary --- p.86Chapter B --- A Trimmable Transconductance Amplifier --- p.87Chapter B.1 --- Introduction --- p.87Chapter B.2 --- Trimmable Transconductance Amplifier using Floating Gate Com- patible Lateral Bipolar Transistors (FG-CLBTs) --- p.87Chapter B.2.1 --- Residual Gate Effect and Collector Current Modulation --- p.89Chapter B.2.2 --- Floating Gate CLBTs --- p.92Chapter B.2.3 --- Electron Tunnelling --- p.93Chapter B.2.4 --- Hot Electron Injection --- p.94Chapter B.2.5 --- Experimental Results of the OTA --- p.94Chapter B.2.6 --- Experimental Results of the FGOTA --- p.96Chapter B.3 --- Summary --- p.97Chapter C --- AMI-ABN 1.5μm n-well Process Parameters (First Batch) --- p.98Chapter D --- AMI-ABN 1.5μm n-well Process Parameters (Second Batch) --- p.101Bibliography --- p.10

A review of current neuromorphic approaches for vision, auditory, and olfactory sensors

Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field

Speech Processing Front-end in Low-power Hardware

The objective of this work is to develop analog integrated circuits to serve as low-power auditory front-ends in signal processing systems. An analog front-end can be used for feature-extraction to reduce the requirements of the digital back-end, or to detect and call attention to compelling characteristics of a signal while the back-end is in sleep mode. Such a front-end should be advantageous for speech recognition, noise suppression, auditory scene analysis, hearing prostheses, biological modeling, or hardware-based event detection.;This work presents a spectral decomposition system, which consists of a bandpass filter bank with sub-band magnitude detection. The bandpass filter is low-power and each channel can be individually programmed for different quality factors and passband gains. The novel magnitude detector has a 68 decibel dynamic range, excellent tracking capability, and consumes less than a microwatt of power. The system, which was fabricated in a 0.18 micron process, consists of a 16-channel filter bank and a variety of sub-band computational elements

Organic bioelectronic devices to control cell signalling

The nervous system consists of a network of specialized cells that coordinate the actions of the body by transmitting information to and from the brain. The communication between the nerve cells is dependent on the interplay of both electrical and chemical signals. As our understanding of nerve cell signalling increases there is a growing need to develop techniques capable of interfacing with the nervous system. One of the major challenges is to translate between the signal carriers of the nervous system (ions and neurotransmitters) and those of conventional electronics (electrons). Organic conjugated polymers represent a unique class of materials that can utilize both electrons and ions as charge carriers. Taking advantage of this combined feature, we have established a novel communication interface between electronic components and biological systems. The organic bioelectronic devices presented in this thesis are based on the organic electronic ion pump (OEIP) made of the conducting organic polymer poly(3,4-ethylenedioxythiophene) doped with poly(styrenesulfonate) (PEDOT:PSS). When electronically addressed, electrochemical redox reactions in the polymer translate electronic signals into electrophoretic migration of ions. We show that the device can transport a range of substances involved in nerve cell signaling. These include positively charged ions, neurotransmitters and cholinergic substances. Since the devices are designed to be easily incorporated in conventional microscopy set-ups, we use Ca2+ imaging as readout to monitor cell responses. We demonstrate how electrophoretic delivery of ions and neurotransmitters with precise, spatiotemporal control can be used to modulate intracellular Ca2+ signaling in neuronal cells in the absence of convective disturbances. The electronic control of delivery enables strict control of dynamic parameters, such as amplitude and frequency of Ca2+ responses, and can be used to generate temporal patterns mimicking naturally occurring Ca2+ oscillations. To enable further control and fine-tuning of the ionic signals we developed the electrophoretic chemical transistor, an analogue of the traditional transistor used to amplify and/or switch electronic signals. We thereby take the first step towards integrated chemical circuits. Finally, we demonstrate the use of the OEIP in a new “machine-to-brain” interface. By encapsulating the OEIP we were able to use it in vivo to modulate brainstem responses in guinea pigs. This was the first successful realization of an organic bioelectronic device capable of modulating mammalian sensory function by precise delivery of neurotransmitters. Our findings highlight the potential of communication interfaces based on conjugated polymers in generating complex, high-resolution, signal patterns to control cell physiology. Such devices will have widespread applications across basic research as well as future applicability in medical devices in multiple therapeutic areas

Neurocomputing systems for auditory processing

This thesis studies neural computation models and neuromorphic implementations of the auditory pathway with applications to cochlear implants and artificial auditory sensory and processing systems. Very low power analogue computation is addressed through the design of micropower analogue building blocks and an auditory preprocessing module targeted at cochlear implants. The analogue building blocks have been fabricated and tested in a standard Complementary Metal Oxide Silicon (CMOS) process. The auditory pre-processing module design is based on the cochlea signal processing mechanisms and low power microelectronic design methodologies. Compared to existing preprocessing techniques used in cochlear implants, the proposed design has a wider dynamic range and lower power consumption. Furthermore, it provides the phase coding as well as the place coding information that are necessary for enhanced functionality in future cochlear implants. The thesis presents neural computation based approaches to a number of signal-processing problems encountered in cochlear implants. Techniques that can improve the performance of existing devices are also presented. Neural network based models for loudness mapping and pattern recognition based channel selection strategies are described. Compared with state—of—the—art commercial cochlear implants, the thesis results show that the proposed channel selection model produces superior speech sound qualities; and the proposed loudness mapping model consumes substantially smaller amounts of memory. Aside from the applications in cochlear implants, this thesis describes a biologically plausible computational model of the auditory pathways to the superior colliculus based on current neurophysiological findings. The model encapsulates interaural time difference, interaural spectral difference, monaural pathway and auditory space map tuning in the inferior colliculus. A biologically plausible Hebbian-like learning rule is proposed for auditory space neural map tuning, and a reinforcement learning method is used for map alignment with other sensory space maps through activity independent cues. The validity of the proposed auditory pathway model has been verified by simulation using synthetic data. Further, a complete biologically inspired auditory simulation system is implemented in software. The system incorporates models of the external ear, the cochlea, as well as the proposed auditory pathway model. The proposed implementation can mimic the biological auditory sensory system to generate an auditory space map from 3—D sounds. A large amount of real 3-D sound signals including broadband White noise, click noise and speech are used in the simulation experiments. The efiect of the auditory space map developmental plasticity is examined by simulating early auditory space map formation and auditory space map alignment with a distorted visual sensory map. Detailed simulation methods, procedures and results are presented

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems

Efficient audio signal processing for embedded systems

We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.PhDCommittee Chair: Anderson, David; Committee Member: Hasler, Jennifer; Committee Member: Hunt, William; Committee Member: Lanterman, Aaron; Committee Member: Minch, Bradle