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

Analogue micropower FET techniques review

A detailed introduction to published analogue circuit design techniques using Si and Si/SiGe FET devices for very low-power applications is presented in this review. The topics discussed include sub-threshold operation in FET devices, micro-current mirrors and cascode techniques, voltage level-shifting and class-AB operation, the bulk-drive approach, the floating-gate method, micropower transconductance-capacitance and log-domain filters and strained-channel FET technologies

Analog adaptive nonlinear filtering and spectral analysis for low-power audio applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, September 2006."August 2006."Includes bibliographical references.Filters are one of the basic building blocks of analog circuits. For linear operation, the power consumption is proportional to the dynamic range for a given topology. I have explored techniques to lower the power consumption below this limit by extending operation beyond the linear range. First, I built a power-efficient linear gm-C filter that demonstrates that dynamic range can be shifted to higher linear ranges using capacitive attenuation. In a standard gm-C filter, the minimum noise is limited by the discrete charge on the electrons and holes stored on the capacitor. This noise can only be reduced by collecting more charge on a larger capacitor, consuming more power. The maximum signal is determined by the linear range of the transconductor. This work showed that both the noise and the maximum signal can be amplified by including a capacitive attenuator in the feedback path of filter. In order to increase the dynamic range, I explored the non-linear operation of the filters, including jump resonance. Unlike harmonic distortion and gain compression which slowly increase with the input amplitude, jump resonance is not present in a linear system, but develops in the presence of strong nonlinearity.(cont.) It is characterized by a discontinuous jump in the frequency response near the resonant peak. I have analyzed the behavior using both describing function and state-space techniques. Then, I developed a novel graphical analysis technique. Finally, I design, built, and tested a circuit for avoiding jump resonance for audio filters. Finally, I took advantage of nonlinearities in a filtering system to build a micropower companding speech processor. This system implements the companding speech processing algorithm to improve speech comprehension in moderate noise environments. The sixteen channel system increases the spectral contrast of speech signals by performing an adjustable two-tone suppression function, replacing the function of a normally function cochlea for hearing aid or cochlear implant users. The system runs on less than 60uW of power, a consumption so low it could run for 6 months on a standard hearing aid battery.by Christopher D. Salthouse.Ph.D

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

Modeling and design of an active silicon cochlea

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references.Silicon cochleas are inspired by the biological cochlea and perform efficient spectrum analysis: They realize a bank of constant-Q Nth-order filters with O(N) efficiency rather than O(N²) efficiency due to their use of an exponentially tapered filter cascade. They are useful in speech-recognition front ends, cochlear implants, and hearing aids, especially as architectures for improving spectral analysis in noisy environments and for performing low-power spectrum analysis. In this thesis I describe four contributions towards improving the state-of-the-art in silicon-cochlea design, two of which involve theoretical modeling, and two of which involve integrated-circuit design. On the theoretical side, I first show that a simple rational approximation to distributed partition impedances in the biological cochlea captures its essential features and enables an efficient artificial implementation achieving maximum gain in a minimum number of stages while still maintaining stability. In particular, I show that the terminating impedance of the cochlea is crucial for its stability and discuss various analytic methods for termination. Second, I derive a novel composite artificial cochlear architecture composed of a cascade of all-pass second-order filters from a first-principles analysis of the biological cochlear transmission line. The novel all-pass architecture reduces phase lag and group delay in the silicon cochlea, a problem in prior designs, sharpens its high-frequency rolloff slopes, increases its frequency selectivity, and improves its nonlinear compression characteristics. On the circuit side, I first present a novel current-mode log-domain topology that simultaneously increases signal-to-noise ratio (SNR) and dynamic range while lowering power consumption in resonant filters with high quality factor Q.(cont.) The novel topology is validated in a second-order low-pass resonant filter, which is employed in the silicon cochlea, demonstrating a reduction in power consumption and increase in SNR by a factor of Q. When bias currents in the filter are adjusted as the signal level varies, this technique enables an improvement in maximum SNR by a factor of Q and an increase in maximum non-distorted signal power and dynamic range by a factor of Q⁴. Measurements from a chip in a 0.18-[mu]m 1.1-V CMOS technology achieve a quiescent power consumption of 580-nW at a 15-kHz center frequency with a maximum SNR of 41.3dB and dynamic range of 76dB for a Q=4. Finally, I describe a current-mode -stage 0.18-[mu]m silicon cochlea that achieves 79dB of dynamic range with 41-[mu]W power consumption on a 1-V power supply over a usable 3.5kHz-14kHz frequency range. These numbers represent an 18dB improvement in dynamic range and a 12.5x reduction in power consumption over prior state-of-the-art silicon cochleas.by Serhii M. Zhak.Ph.D

Analogue micropower FET techniques review

Accepted versio

Chemical Bionics - a novel design approach using ion sensitive field effect transistors

In the late 1980s Carver Mead introduced Neuromorphic engineering in which various aspects of the neural systems of the body were modelled using VLSI1 circuits. As a result most bio-inspired systems to date concentrate on modelling the electrical behaviour of neural systems such as the eyes, ears and brain. The reality is however that biological systems rely on chemical as well as electrical principles in order to function. This thesis introduces chemical bionics in which the chemically-dependent physiology of specific cells in the body is implemented for the development of novel bio-inspired therapeutic devices. The glucose dependent pancreatic beta cell is shown to be one such cell, that is designed and fabricated to form the first silicon metabolic cell. By replicating the bursting behaviour of biological beta cells, which respond to changes in blood glucose, a bio-inspired prosthetic for glucose homeostasis of Type I diabetes is demonstrated. To compliment this, research to further develop the Ion Sensitive Field Effect Transistor (ISFET) on unmodified CMOS is also presented for use as a monolithic sensor for chemical bionic systems. Problems arising by using the native passivation of CMOS as a sensing surface are described and methods of compensation are presented. A model for the operation of the device in weak inversion is also proposed for exploitation of its physical primitives to make novel monolithic solutions. Functional implementations in various technologies is also detailed to allow future implementations chemical bionic circuits. Finally the ISFET integrate and fire neuron, which is the first of its kind, is presented to be used as a chemical based building block for many existing neuromorphic circuits. As an example of this a chemical imager is described for spatio-temporal monitoring of chemical species and an acid base discriminator for monitoring changes in concentration around a fixed threshold is also proposed

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

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits