Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

http://eia.udg.es/%7Eqsalvi/papers/2006-MJ.pdf

Review of CMOS image sensors

A realistic power consumption model of wireless communication subsystems typically used in many sensor network node devices is presented. Simple power consumption models for major components are individually identified, and the effective transmission range of a sensor node is modeled by the output power of the transmitting power amplifier, sensitivity of the receiving low noise amplifier, and RF environment. Using this basic model, conditions for minimum sensor network power consumption are derived for communication of sensor data from a source device to a destination node. Power consumption model parameters are extracted for two types of wireless sensor nodes that are widely used and commercially available. For typical hardware configurations and RF environments, it is shown that whenever single hop routing is possible it is almost always more power efficient than multi-hop routing. Further consideration of communication protocol overhead also shows that single hop routing will be more power efficient compared to multi-hop routing under realistic circumstances. This power consumption model can be used to guide design choices at many different layers of the design space including, topology design, node placement, energy efficient routing schemes, power management and the hardware design of future wireless sensor network devices

A Realistic Power Consumption Model for Wireless Sensor Network Devices

This paper examines error control coding (ECC) use in wireless sensor networks (WSNs) to determine the energy efficiency of specific ECC implementations in WSNs. ECC provides coding gain, resulting in transmitter energy savings, at the cost of added decoder power consumption. This paper derives an expression for the critical distance dCR, the distance at which the decoder's energy consumption per bit equals the transmit energy savings per bit due to coding gain, compared to an uncoded system. Results for several decoder implementations, both analog and digital, are presented for dCR in different environments over a wide frequency range. In free space, dCR is very large at lower frequencies, suitable only for widely spaced outdoor sensors. In crowded environments and office buildings, dCR drops significantly, to 3 m or greater at 10 GHz. Interference is not considered; it would lower dCR. Analog decoders areshown to be the mostenergy-efficient decoders in this study.

/pdf/error-control-coding-in-low-power-wireless-sensor-networks-2ch6el76ut.pdf

Error control coding in low-power wireless sensor networks: when is ECC energy-efficient?

This expanded and thoroughly revised edition of Thomas H. Lee's acclaimed guide to the design of gigahertz RF integrated circuits features a completely new chapter on the principles of wireless systems. The chapters on low-noise amplifiers, oscillators and phase noise have been significantly expanded as well. The chapter on architectures now contains several examples of complete chip designs that bring together all the various theoretical and practical elements involved in producing a prototype chip. First Edition Hb (1998): 0-521-63061-4 First Edition Pb (1998); 0-521-63922-0

The Design of CMOS Radio-Frequency Integrated Circuits: RF CIRCUITS THROUGH THE AGES

Bootstrapped switches are used in a variety of applications including DC-DC converters, pipelined analog-to-digital converters and high voltage switches and drivers. Current work on highly integrated power management applications often requires the ability to measure voltage quantities that exceed the supply voltage in magnitude. This is primarily due to a basic need to maximize efficiency by running the power management IC on as low supply voltage as possible, while still maintaining the ability to sample and measure quantities from the surroundings that could well exceed the battery voltage. In this paper, a new bootstrapped switch is presented. The switch enables the precise sampling of input signals well greater than the chip supply voltage with no static power consumption, and without activating on-chip parasitic body diodes. The bootstrapped switch, presented here, is designed to sample an input signal with a 0-5.5-V range at a supply voltage of 2.75 V. Measurement data shows functionality for a 0-6-V input signal range with a supply voltage as low as 1.2 V

/pdf/switch-bootstrapping-for-precise-sampling-beyond-supply-2upzz8p7z7.pdf

Switch Bootstrapping for Precise Sampling Beyond Supply Voltage

A low drop-out voltage regulator having soft-start. A low drop-out regulator circuit is provided having an input node, an output node, a power FET connected by a source and drain between the input node and the output node, and a feedback circuit having an output connected and providing a control signal to a gate of the power FET. A current limit circuit is configured to control the power FET to limit the current through it when the voltage across a controllable sense resistor connected to conduct a current representing the current through the power FET exceeds a predetermined limit value. At start-up, control unit provides a control signal to the controllable resistor to cause the resistance value of the controllable resistor to decrease incrementally in value at respective predetermined incremental times during a predetermined time interval.

Soft-start circuit and method for low-dropout voltage regulators

The bootstrapping circuit capable of sampling inputs beyond supply voltage includes: a bootstrapped switch (MN20) coupled between an input node and an output node; a first transistor (MP 13) having a first end coupled to a control node of the bootstrapped switch; a clock bootstrapped capacitor (C 13) having a first end coupled to a second end of the first transistor; a second transistor (MN27) coupled between the first end of the first transistor and a supply node, and having a control node coupled to a first clock signal node PHI; a third transistor (MN26) coupled between the second end of the first transistor and the supply node; a charge pump having a first output coupled to a control node of the third transistor; a level shifter having a first output coupled to a second end of the clock bootstrapped capacitor; a fourth transistor (MN25) coupled between the supply node and a control node of the first transistor, and having a control node coupled to a second output of the charge pump; a capacitor coupled between a second output of the level shifter and a control node of the first transistor; and a fifth transistor (MN28) coupled between the control node of the bootstrapped switch and a common node.

Bootstrapping circuit capable of sampling inputs beyond supply voltage

This brief describes a low-power current-mode voltage reference based on subthreshold transistors. A novel circuit configuration together with a high-order temperature compensation scheme allow this voltage reference to operate with a supply voltage down to 0.4 V over a large temperature range (from −40 °C to 130 °C). The circuit, which is fabricated with a standard 0.18- $\mu\text{m}$ CMOS technology, provides an output voltage of 212 mV, while consuming 192 nW. The measured average temperature coefficient is 84.5 ppm/°C.

A 0.4-V Supply Curvature-Corrected Reference Generator With 84.5-ppm/°C Average Temperature Coefficient Within −40 °C to 130 °C

Bootstrapped switches are used in a variety of applications including DC-DC converters, pipelined analog-to-digital converters and high voltage switches and drivers. Current work on highly integrated power management applications often requires the ability to measure voltage quantities that exceed the supply voltage in magnitude. This is primarily due to a basic need to maximize efficiency by running the power management IC on as low supply voltage as possible, while still maintaining the ability to sample and measure quantities from the surroundings that could well exceed the battery voltage. In this paper a new bootstrapped switch is presented. The switch enables the precise sampling of input signals well greater than the chip supply voltage with no static power consumption, and without activating on-chip parasitic body diodes. The bootstrapped switch, presented here, is designed to sample an input signal with a 0-5.5 V range at a supply voltage of 2.75 V. Measurement data shows functionality for a 0-6 V input signal range with a supply voltage as low as 1.2 V

/pdf/a-bootstrapped-switch-for-precise-sampling-of-inputs-with-22pdv30p4a.pdf

Devrim Yilmaz Aksin

Papers

Switch Bootstrapping for Precise Sampling Beyond Supply Voltage

Soft-start circuit and method for low-dropout voltage regulators

Bootstrapping circuit capable of sampling inputs beyond supply voltage

A 0.4-V Supply Curvature-Corrected Reference Generator With 84.5-ppm/°C Average Temperature Coefficient Within −40 °C to 130 °C

A bootstrapped switch for precise sampling of inputs with signal range beyond supply voltage