Implementing neural architectures using analog VLSI circuits

Analog very large-scale integrated (VLSI) technology can be used not only to study and simulate biological systems, but also to emulate them in designing artificial sensory systems. A methodology for building these systems in CMOS VLSI technology has been developed using analog micropower circuit elements that can be hierarchically combined. Using this methodology, experimental VLSI chips of visual and motor subsystems have been designed and fabricated. These chips exhibit behavior similar to that of biological systems, and perform computations useful for artificial sensory systems

Winner-take-all in a phase oscillator system with adaptation.

We consider a system of generalized phase oscillators with a central element and radial connections. In contrast to conventional phase oscillators of the Kuramoto type, the dynamic variables in our system include not only the phase of each oscillator but also the natural frequency of the central oscillator, and the connection strengths from the peripheral oscillators to the central oscillator. With appropriate parameter values the system demonstrates winner-take-all behavior in terms of the competition between peripheral oscillators for the synchronization with the central oscillator. Conditions for the winner-take-all regime are derived for stationary and non-stationary types of system dynamics. Bifurcation analysis of the transition from stationary to non-stationary winner-take-all dynamics is presented. A new bifurcation type called a Saddle Node on Invariant Torus (SNIT) bifurcation was observed and is described in detail. Computer simulations of the system allow an optimal choice of parameters for winner-take-all implementation

Analog VLSI implementation for stereo correspondence between 2-D images

Many robotics and navigation systems utilizing stereopsis to determine depth have rigid size and power constraints and require direct physical implementation of the stereo algorithm. The main challenges lie in managing the communication between image sensor and image processor arrays, and in parallelizing the computation to determine stereo correspondence between image pixels in real-time. This paper describes the first comprehensive system level demonstration of a dedicated low-power analog VLSI (very large scale integration) architecture for stereo correspondence suitable for real-time implementation. The inputs to the implemented chip are the ordered pixels from a stereo image pair, and the output is a two-dimensional disparity map. The approach combines biologically inspired silicon modeling with the necessary interfacing options for a complete practical solution that can be built with currently available technology in a compact package. Furthermore, the strategy employed considers multiple factors that may degrade performance, including the spatial correlations in images and the inherent accuracy limitations of analog hardware, and augments the design with countermeasures

A CMOS-based Hartmann-Shack Sensor for Real-Time Adaptive Optical Applications

Adaptive optical systems have a growing field of applications in opthalmology. In every adaptive system there is the need for a sensor and an actuator. The Hartmann-Shack wavefront sensor uses the displacement of spots in the focal plane of a lenslet array for subsequent calculation of the wavefront. The bandwidth of current sensors is mostly limited by software processing of the focal plane image to some tens of Hz, which makes it unsuitable for real-time adaptive optical systems. To overcome the current bandwidth limitations a fast Hartmann-Shack sensor based on an application specific integrated circuit has been developed and tested, that reaches a bandwidth of up to 6 kHz. The sensor includes photodetectors with 40% quantum efficiency at 680 nm wavelength and an image processing, that is especially suitable to reduce the effects of the common mismatching of process parameters in CMOS-based sensors (Complementary Metal Oxide Semiconductor). A special problem in ophthalmic applications is the low available spot power below 1 nW.Adaptive optical systems have a growing field of applications in opthalmology. In every adaptive system there is the need for a sensor and an actuator. The Hartmann-Shack wavefront sensor uses the displacement of spots in the focal plane of a lenslet array for subsequent calculation of the wavefront. The bandwidth of current sensors is mostly limited by software processing of the focal plane image to some tens of Hz, which makes it unsuitable for real-time adaptive optical systems. To overcome the current bandwidth limitations a fast Hartmann-Shack sensor based on an application specific integrated circuit has been developed and tested, that reaches a bandwidth of up to 6 kHz. The sensor includes photodetectors with 40% quantum efficiency at 680 nm wavelength and an image processing, that is especially suitable to reduce the effects of the common mismatching of process parameters in CMOS-based sensors (Complementary Metal Oxide Semiconductor). A special problem in ophthalmic applications is the low available spot power below 1 nW. The developed Hartmann-Shack sensor allowed wavefront measurements with an accuracy of 0.16 dpt defocus at 160 pW spot power. It has been possible for the first time, to measure wavefront aberrations at the living humane eye with 300 Hz repetition rate and to calculate the power spectral density of these aberrations

Synthesis of neuromorphic circuits with neuromodulatory properties

The field of neuromorphic engineering shows great promise in delivering novel devices inspired by biological principles that would undertake sensory and processing tasks with an unprecedented level of efficiency. In order to achieve that, engineers are required to understand and implement the many complex biological regulatory mechanisms that allow the nervous system to robustly operate and adapt over scales covering many orders of magnitude, while at the same time using unreliable and noisy components. As a step towards that, this thesis aims at discussing and implementing the principles of neuromodulation in neuromorphic hardware, mechanisms which allow neurons to change and regulate their behaviour through the continuous control of their internal currents. We discuss how neural dynamics and its modulation can be broken down into four essential feedback loops, and we introduce a simplified model of the neural membrane respecting this fundamental structure. We present a novel methodology for controlling the neuron's behaviour through the shaping of its I-V curves in distinct timescales, thus characterising the behaviour of the neural circuit through its input-output properties. We show how modulation of the feedback loops affects the behaviour, and importantly, captures the transition between spiking and bursting oscillatory regimes, two major signalling modes of neurons. We then show how the architecture can be easily implemented using well-known neuromorphic building blocks based on subthreshold MOSFET circuits. Finally, we discuss how the excitability switch captured by the model can be exploited in simple network settings, thus opening up the possibility for future research into novel architectures where the control of cellular properties is utilised to shape the global behaviour of the network

Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

Neuromorphic perception for greenhouse technology using event-based sensors

Event-Based Cameras (EBCs), unlike conventional cameras, feature independent pixels that asynchronously generate outputs upon detecting changes in their field of view. Short calculations are performed on each event to mimic the brain. The output is a sparse sequence of events with high temporal precision. Conventional computer vision algorithms do not leverage these properties. Thus a new paradigm has been devised. While event cameras are very efficient in representing sparse sequences of events with high temporal precision, many approaches are challenged in applications where a large amount of spatially-temporally rich information must be processed in real-time. In reality, most tasks in everyday life take place in complex and uncontrollable environments, which require sophisticated models and intelligent reasoning. Typical hard problems in real-world scenes are detecting various non-uniform objects or navigation in an unknown and complex environment. In addition, colour perception is an essential fundamental property in distinguishing objects in natural scenes. Colour is a new aspect of event-based sensors, which work fundamentally differently from standard cameras, measuring per-pixel brightness changes per colour filter asynchronously rather than measuring “absolute” brightness at a constant rate. This thesis explores neuromorphic event-based processing methods for high-noise and cluttered environments with imbalanced classes. A fully event-driven processing pipeline was developed for agricultural applications to perform fruits detection and classification to unlock the outstanding properties of event cameras. The nature of features in such data was explored, and methods to represent and detect features were demonstrated. A framework for detecting and classifying features was developed and evaluated on the N-MNIST and Dynamic Vision Sensor (DVS) gesture datasets. The same network was evaluated on laboratory recorded and real-world data with various internal variations for fruits detection such as overlap, variation in size and appearance. In addition, a method to handle highly imbalanced data was developed. We examined the characteristics of spatio-temporal patterns for each colour filter to help expand our understanding of this novel data and explored their applications in classification tasks where colours were more relevant features than shapes and appearances. The results presented in this thesis demonstrate the potential and efficacy of event- based systems by demonstrating the applicability of colour event data and the viability of event-driven classification