Low Voltage Floating Gate MOS Transistor Based Four-Quadrant Multiplier

This paper presents a four-quadrant multiplier based on square-law characteristic of floating gate MOSFET (FGMOS) in saturation region. The proposed circuit uses square-difference identity and the differential voltage squarer proposed by Gupta et al. to implement the multiplication function. The proposed multiplier employs eight FGMOS transistors and two resistors only. The FGMOS implementation of the multiplier allows low voltage operation, reduced power consumption and minimum transistor count. The second order effects caused due to mobility degradation, component mismatch and temperature variations are discussed. Performance of the proposed circuit is verified at ±0.75 V in TSMC 0.18 µm CMOS, BSIM3 and Level 49 technology by using Cadence Spectre simulator

A Bio-Inspired Two-Layer Mixed-Signal Flexible Programmable Chip for Early Vision

A bio-inspired model for an analog programmable array processor (APAP), based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, what renders a feasible alternative for the implementation of early vision tasks in standard technologies. A prototype chip has been designed and fabricated in 0.5 μm CMOS. It renders a computing power per silicon area and power consumption that is amongst the highest reported for a single chip. The details of the bio-inspired network model, the analog building block design challenges and trade-offs and some functional tests results are presented in this paper.Office of Naval Research (USA) N-000140210884European Commission IST-1999-19007Ministerio de Ciencia y Tecnología TIC1999-082

Second-order neural core for bioinspired focal-plane dynamic image processing in CMOS

Based on studies of the mammalian retina, a bioinspired model for mixed-signal array processing has been implemented on silicon. This model mimics the way in which images are processed at the front-end of natural visual pathways, by means of programmable complex spatio-temporal dynamic. When embedded into a focal-plane processing chip, such a model allows for online parallel filtering of the captured image; the outcome of such processing can be used to develop control feedback actions to adapt the response of photoreceptors to local image features. Beyond simple resistive grid filtering, it is possible to program other spatio-temporal processing operators into the model core, such as nonlinear and anisotropic diffusion, among others. This paper presents analog and mixed-signal very large-scale integration building blocks to implement this model, and illustrates their operation through experimental results taken from a prototype chip fabricated in a 0.5-μm CMOS technology.European Union IST 2001 38097Ministerio de Ciencia y Tecnología TIC 2003 09817 C02 01Office of Naval Research (USA) N00014021088

Programmable retinal dynamics in a CMOS mixed-signal array processor chip

The low-level image processing that takes place in the retina is intended to compress the relevant visual information to a manageable size. The behavior of the external layers of the biological retina has been successfully modelled by a Cellular Neural Network, whose evolution can be described by a set of coupled nonlinear differential equations. A mixed-signal VLSI implementation of the focal-plane low-level image processing based upon this biological model constitutes a feasible and cost effective alternative to conventional digital processing in real-time applications. For these reasons, a programmable array processor prototype chip has been designed and fabricated in a standard 0.5μm CMOS technology. The integrated system consists of a network of two coupled layers, containing 32 × 32 elementary processors, running at different time constants. Involved image processing algorithms can be programmed on this chip by tuning the appropriate interconnections weights. Propagative, active wave phenomena and retina-like effects can be observed in this chip. Design challenges, trade-offs, the buildings blocks and some test results are presented in this paper.Office of Naval Research (USA) N00014-00-10429European Community IST-1999-19007Ministerio de Ciencia y Tecnología TIC1999-082

A New Subthreshold Current-Mode Four Quadrant Multiplier

This paper presents a novel current mode four quadrant multiplier. A pair of sub threshold translinear loops and current conveyors are the basic building blocks in realization scheme. The proposed multiplier features simplicity, low power dissipation. The salient features of this approach are; it?s single ended inputs; since it uses sub threshold region of operation, this make the design interesting for low power application; current mode application yields large dynamic range and low power dissipation

Integrated optical motion detection

Two systems for detecting the motion of a scene are described. For both, an image is projected directly onto an integrated circuit that contains photosensors and computing circuitry to extract the motion. The first system, which has been reported earlier, correlates the analog image with a digitized version of the image stored from the previous cycle. The chip reports the motion that corresponds to the maximum analog correlation value. This system represents an advance from previous designs but exhibits some shortcomings. A second completely analog design surpasses the first. The mathematical foundation is derived and the CMOS circuits used in the implementation are given. Test results and characterization of the working chips are reported. The new motion detector is not clocked and exhibits collective behavior. The extensive use of local information avoids the correspondence problem. The system can be thought of as a Hopfield neural net with one important extension--input-driven synapses. The motion detector also meshes nicely with the existing computational vision work. Extensions to handle more complex motions are proposed. The suitability of the motion-extraction algorithm as a biological vision model is explored

Power-efficient current-mode analog circuits for highly integrated ultra low power wireless transceivers

In this thesis, current-mode low-voltage and low-power techniques have been applied to implement novel analog circuits for zero-IF receiver backend design, focusing on amplification, filtering and detection stages. The structure of the thesis follows a bottom-up scheme: basic techniques at device level for low voltage low power operation are proposed in the first place, followed by novel circuit topologies at cell level, and finally the achievement of new designs at system level. At device level the main contribution of this work is the employment of Floating-Gate (FG) and Quasi-Floating-Gate (QFG) transistors in order to reduce the power consumption. New current-mode basic topologies are proposed at cell level: current mirrors and current conveyors. Different topologies for low-power or high performance operation are shown, being these circuits the base for the system level designs. At system level, novel current-mode amplification, filtering and detection stages using the former mentioned basic cells are proposed. The presented current-mode filter makes use of companding techniques to achieve high dynamic range and very low power consumption with for a very wide tuning range. The amplification stage avoids gain bandwidth product achieving a constant bandwidth for different gain configurations using a non-linear active feedback network, which also makes possible to tune the bandwidth. Finally, the proposed current zero-crossing detector represents a very power efficient mixed signal detector for phase modulations. All these designs contribute to the design of very low power compact Zero-IF wireless receivers. The proposed circuits have been fabricated using a 0.5μm double-poly n-well CMOS technology, and the corresponding measurement results are provided and analyzed to validate their operation. On top of that, theoretical analysis has been done to fully explore the potential of the resulting circuits and systems in the scenario of low-power low-voltage applications.Programa Oficial de Doctorado en Tecnologías de las Comunicaciones (RD 1393/2007)Komunikazioen Teknologietako Doktoretza Programa Ofiziala (ED 1393/2007

Digitally Interfaced Analog Correlation Filter System for Object Tracking Applications

Advanced correlation filters have been employed in a wide variety of image processing and pattern recognition applications such as automatic target recognition and biometric recognition. Among those, object recognition and tracking have received more attention recently due to their wide range of applications such as autonomous cars, automated surveillance, human-computer interaction, and vehicle navigation.Although digital signal processing has long been used to realize such computational systems, they consume extensive silicon area and power. In fact, computational tasks that require low to moderate signal-to-noise ratios are more efficiently realized in analog than digital. However, analog signal processing has its own caveats. Mainly, noise and offset accumulation which degrades the accuracy, and lack of a scalable and standard input/output interface capable of managing a large number of analog data.Two digitally-interfaced analog correlation filter systems are proposed. While digital interfacing provided a standard and scalable way of communication with pre- and post-processing blocks without undermining the energy efficiency of the system, the multiply-accumulate operations were performed in analog. Moreover, non-volatile floating-gate memories are utilized as storage for coefficients. The proposed systems incorporate techniques to reduce the effects of analog circuit imperfections.The first system implements a 24x57 Gilbert-multiplier-based correlation filter. The I/O interface is implemented with low-power D/A and A/D converters and a correlated double sampling technique is implemented to reduce offset and lowfrequency noise at the output of analog array. The prototype chip occupies an area of 3.23mm2 and demonstrates a 25.2pJ/MAC energy-efficiency at 11.3 kVec/s and 3.2% RMSE.The second system realizes a 24x41 PWM-based correlation filter. Benefiting from a time-domain approach to multiplication, this system eliminates the need for explicit D/A and A/D converters. Careful utilization of clock and available hardware resources in the digital I/O interface, along with application of power management techniques has significantly reduced the circuit complexity and energy consumption of the system. Additionally, programmable transconductance amplifiers are incorporated at the output of the analog array for offset and gain error calibration. The prototype system occupies an area of 0.98mm2 and is expected to achieve an outstanding energy-efficiency of 3.6pJ/MAC at 319kVec/s with 0.28% RMSE

FEEDFORWARD ARTIFICIAL NEURAL NETWORK DESIGN UTILISING SUBTHRESHOLD MODE CMOS DEVICES

This thesis reviews various previously reported techniques for simulating artificial neural networks and investigates the design of fully-connected feedforward networks based on MOS transistors operating in the subthreshold mode of conduction as they are suitable for performing compact, low power, implantable pattern recognition systems. The principal objective is to demonstrate that the transfer characteristic of the devices can be fully exploited to design basic processing modules which overcome the linearity range, weight resolution, processing speed, noise and mismatch of components problems associated with weak inversion conduction, and so be used to implement networks which can be trained to perform practical tasks. A new four-quadrant analogue multiplier, one of the most important cells in the design of artificial neural networks, is developed. Analytical as well as simulation results suggest that the new scheme can efficiently be used to emulate both the synaptic and thresholding functions. To complement this thresholding-synapse, a novel current-to-voltage converter is also introduced. The characteristics of the well known sample-and-hold circuit as a weight memory scheme are analytically derived and simulation results suggest that a dummy compensated technique is required to obtain the required minimum of 8 bits weight resolution. Performance of the combined load and thresholding-synapse arrangement as well as an on-chip update/refresh mechanism are analytically evaluated and simulation studies on the Exclusive OR network as a benchmark problem are provided and indicate a useful level of functionality. Experimental results on the Exclusive OR network and a 'QRS' complex detector based on a 10:6:3 multilayer perceptron are also presented and demonstrate the potential of the proposed design techniques in emulating feedforward neural networks