A Focal-Plane Image Processor for Low Power Adaptive Capture and Analysis of the Visual Stimulus

Portable applications of artificial vision are limited by the fact that conventional processing schemes fail to meet the specifications under a tight power budget. A bio-inspired approach, based in the goal-directed organization of sensory organs found in nature, has been employed to implement a focal-plane image processor for low power vision applications. The prototype contains a multi-layered CNN structure concurrent with 32times32 photosensors with locally programmable integration time for adaptive image capture with on-chip local and global adaptation mechanisms. A more robust and linear multiplier block has been employed to reduce irregular analog wave propagation ought to asymmetric synapses. The predicted computing power per power consumption, 142MOPS/mW, is orders of magnitude above what rendered by conventional architectures

Implementing Homeostatic Plasticity in Analog VLSI

Neuromorphic engineering systems are electronic devices that emulate the spike based computational paradigm. CMOS processes scaling yield mismatch and non-ideality that limit the performances of the device. A neuromorphic approach to address this problem is to implement the SHP in silicon. The SHP is implemented by an AGC with a LPF with long time constants. Given such LPF challenging specifications, I developed a compact CMOS filter architecture based on leakages currents in a pMOS deviceopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil

Power Management ICs for Internet of Things, Energy Harvesting and Biomedical Devices

This dissertation focuses on the power management unit (PMU) and integrated circuits (ICs) for the internet of things (IoT), energy harvesting and biomedical devices. Three monolithic power harvesting methods are studied for different challenges of smart nodes of IoT networks. Firstly, we propose that an impedance tuning approach is implemented with a capacitor value modulation to eliminate the quiescent power consumption. Secondly, we develop a hill-climbing MPPT mechanism that reuses and processes the information of the hysteresis controller in the time-domain and is free of power hungry analog circuits. Furthermore, the typical power-performance tradeoff of the hysteresis controller is solved by a self-triggered one-shot mechanism. Thus, the output regulation achieves high-performance and yet low-power operations as low as 12 µW. Thirdly, we introduce a reconfigurable charge pump to provide the hybrid conversion ratios (CRs) as 1⅓× up to 8× for minimizing the charge redistribution loss. The reconfigurable feature also dynamically tunes to maximum power point tracking (MPPT) with the frequency modulation, resulting in a two-dimensional MPPT. Therefore, the voltage conversion efficiency (VCE) and the power conversion efficiency (PCE) are enhanced and flattened across a wide harvesting range as 0.45 to 3 V. In a conclusion, we successfully develop an energy harvesting method for the IoT smart nodes with lower cost, smaller size, higher conversion efficiency, and better applicability. For the biomedical devices, this dissertation presents a novel cost-effective automatic resonance tracking method with maximum power transfer (MPT) for piezoelectric transducers (PT). The proposed tracking method is based on a band-pass filter (BPF) oscillator, exploiting the PT’s intrinsic resonance point through a sensing bridge. It guarantees automatic resonance tracking and maximum electrical power converted into mechanical motion regardless of process variations and environmental interferences. Thus, the proposed BPF oscillator-based scheme was designed for an ultrasonic vessel sealing and dissecting (UVSD) system. The sealing and dissecting functions were verified experimentally in chicken tissue and glycerin. Furthermore, a combined sensing scheme circuit allows multiple surgical tissue debulking, vessel sealer and dissector (VSD) technologies to operate from the same sensing scheme board. Its advantage is that a single driver controller could be used for both systems simplifying the complexity and design cost. In a conclusion, we successfully develop an ultrasonic scalpel to replace the other electrosurgical counterparts and the conventional scalpels with lower cost and better functionality

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

Design of a Low Power 70MHz-110MHz Harmonic Rejection Filter with Class-AB Output Stage

An FM transmitter becomes the new feature in recent portable electronic development. A low power, integrable FM transmitter filter IC is required to meet the demand of FM transmitting feature. A low pass filter using harmonic rejection technique along with a low power class-AB output buffer is designed to meet the current market requirements on the FM transmitter chip. A harmonic rejection filter is designed to filter FM square wave signal from 70MHz to 110MHz into FM sine wave signal. Based on Fourier series, the harmonic rejection technique adds the phase shifted square waves to achieve better THD and less high frequency harmonics. The phase shifting is realized through a frequency divider, and the summation is implemented through a current summation circuit. A RC low pass filter with automatic tuning is designed to further attenuate unwanted harmonics. In this work, the filter's post layout simulation shows -53dB THD and harmonics above 800MHz attenuation of -99dB. The power consumption of the filter is less than 0.7mW. Output buffer stage is implemented through a resistor degenerated transconductor and a class-AB amplifier. Feedforward frequency compensation is applied to compensate the output class-AB stage, which extends the amplifier's operating bandwidth. A fully balanced class-AB driver is proposed to unleash the driving capability of common source output transistors. The output buffer reaches -43dB THD at 110MHz with 0.63Vpp output swing and drives 1mW into 50 load. The power consumption of the output buffer is 7.25mW. By using harmonic rejection technique, this work realizes the 70MHz-110MHz FM carrier filtering using TSMC 0.18um nominal process. Above 800MHz harmonics are attenuated to below -95dB. With 1.2V supply, the total power consumption including output buffer is 7.95mW. The total die area is 0.946mm2

Low-voltage, low-power circuits for data communication systems

There are growing industrial demands for low-voltage supply and low-power consumption circuits and systems. This is especially true for very high integration level and very large scale integrated (VLSI) mixed-signal chips and system-on-a-chip. It is mainly due to the limited power dissipation within a small area and the costs related to the packaging and thermal management. In this research work, two low-voltage, low-power integrated circuits used for data communication systems are introduced. The first one is a high performance continuous-time linear phase filter with automatic frequency tuning. The filter can be used in hard disk driver systems and wired communication systems such as 1000Base-T transceivers. A pseudo-differential operational transconductance amplifier (OTA) based on transistors operating in triode region is used to achieve a large linear signal swing with low-voltage supplies. A common-mode (CM) control circuit that combines common-mode feedback (CMFB), common-mode feedforward (CMFF), and adaptive-bias has been proposed. With a 2.3V single supply, the filters total harmonic distortion is less than 44dB for a 2VPP differential input, which is due to the well controlled CM behavior. The ratio of the root mean square value of the ac signal to the power supply voltage is around 31%, which is much better than previous realizations. The second integrated circuit includes two LVDS drivers used for high-speed point-to-point links. By removing the stacked switches used in the conventional structures, both LVDS drivers can operate with ultra low-voltage supplies. Although the Double Current Sources (DCS) LVDS driver draws twice minimum static current as required by the signal swing, it is quite simple and achieves very high speed operation. The Switchable Current Sources (SCS) LVDS driver, by dynamically switching the current sources, draws minimum static current and reduces the power consumption by 60% compared to the previously reported LVDS drivers. Both LVDS drivers are compliant to the standards and operate at data rates up to gigabits-per-second

Techniques of Energy-Efficient VLSI Chip Design for High-Performance Computing

How to implement quality computing with the limited power budget is the key factor to move very large scale integration (VLSI) chip design forward. This work introduces various techniques of low power VLSI design used for state of art computing. From the viewpoint of power supply, conventional in-chip voltage regulators based on analog blocks bring the large overhead of both power and area to computational chips. Motivated by this, a digital based switchable pin method to dynamically regulate power at low circuit cost has been proposed to make computing to be executed with a stable voltage supply. For one of the widely used and time consuming arithmetic units, multiplier, its operation in logarithmic domain shows an advantageous performance compared to that in binary domain considering computation latency, power and area. However, the introduced conversion error reduces the reliability of the following computation (e.g. multiplication and division.). In this work, a fast calibration method suppressing the conversion error and its VLSI implementation are proposed. The proposed logarithmic converter can be supplied by dc power to achieve fast conversion and clocked power to reduce the power dissipated during conversion. Going out of traditional computation methods and widely used static logic, neuron-like cell is also studied in this work. Using multiple input floating gate (MIFG) metal-oxide semiconductor field-effect transistor (MOSFET) based logic, a 32-bit, 16-operation arithmetic logic unit (ALU) with zipped decoding and a feedback loop is designed. The proposed ALU can reduce the switching power and has a strong driven-in capability due to coupling capacitors compared to static logic based ALU. Besides, recent neural computations bring serious challenges to digital VLSI implementation due to overload matrix multiplications and non-linear functions. An analog VLSI design which is compatible to external digital environment is proposed for the network of long short-term memory (LSTM). The entire analog based network computes much faster and has higher energy efficiency than the digital one

3-Layer CNN Chip for Focal-Plane Complex Dynamics with Adaptive Image Capture

This paper presents a CMOS implementation of a layered CNN concurrent with 32times32 photosensors with locally programmable integration time for adaptive image capture. The network is arranged in two layers containing feedback and control templates, inter-layer connections and programmable ratio of time constants. There are also feedforward connections to a third layer, which is faster, and devoted exclusively for combining the outputs of the other two. A more robust and linear multiplier block has been employed to reduce irregular analog wave propagation ought to asymmetric synapses. Global and local adaptation circuits are included on-chip. The predicted computing power per power consumption, 240MOPS/mW, is amongst the largest reported, what renders this kind of devices as especially adequate for portable applications of artificial visionMinisterio de Ciencia y Tecnología TIC2003-09817-C02-01Office of Naval (USA) N-00014-02-1-088