I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

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Art of electronics

Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are meeting hard physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Towards this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.

/pdf/finding-a-roadmap-to-achieve-large-neuromorphic-hardware-4vszjr2j41.pdf

Finding a roadmap to achieve large neuromorphic hardware systems.

The number of nodes typically used in sensory networks is growing rapidly, leading to large amounts of redundant data being exchanged between sensory terminals and computing units. To efficiently process such large amounts of data, and decrease power consumption, it is necessary to develop approaches to computing that operate close to or inside sensory networks, and that can reduce the redundant data movement between sensing and processing units. Here we examine the concept of near-sensor and in-sensor computing in which computation tasks are moved partly to the sensory terminals. We classify functions into low-level and high-level processing, and discuss the implementation of near-sensor and in-sensor computing for different physical sensing systems. We also analyse the existing challenges in the field and provide possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies. This Perspective examines the concept of near-senor and in-sensor computing in which computation tasks are moved partly to the sensory terminals, exploring the challenges facing the field and providing possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies.

Near-sensor and in-sensor computing

Floating-gate (FG) transistors are useful for precisely programming a large array of current sources. Present FG programming techniques require disconnection of the transistor from the rest of its circuit while it is being programmed. We present a new method of programming FG transistors that does not require this disconnection. In this indirect programming method, two transistors share a FG allowing one to exist directly in a circuit while the other is reserved for programming. Since the transistor does not need to be disconnected from the circuit to program it, the switch count is reduced, resulting in fewer parasitics and better overall performance. Additionally, the use of these indirectly programmed FG transistors allows a circuit to be tuned such that the effects of device mismatch are negated. Finally, the concept of run-time programming is introduced which allows a circuit to be recalibrated while it is still operating within its system

Indirect Programming of Floating-Gate Transistors

We present a programmable, continuous-time bandpass filter that is extremely compact, power efficient, and can cover a wide range of frequencies (10 Hz-10 MHz). This capacitively coupled current conveyor (C 4) has a second-order bandpass transfer function and is capable of being used as a basic bandpass-filter element to create high-order filters. The use of floating-gate transistors helps to ease the difficulties of effectively utilizing G m-C filters by providing precise, programmable current sources that set the filter's time constants. Additionally, we provide an algorithmic design approach for constructing these bandpass filters to meet any given specifications. This bandpass filter is ideally suited to large filter-bank applications because of its small size and low-power demands.

A Low-Power Programmable Bandpass Filter Section for Higher Order Filter Applications

Preprocessing of data before transmission is recommended for many sensor network applications to reduce communication and improve energy efficiency. However, constraints on memory, speed, and energy currently limit the processing capabilities within a sensor network. In this paper, we describe how ultra-low-power analog circuitry can be integrated with sensor nodes to create energy-efficient sensor networks. To demonstrate this concept, we present a custom analog front-end which performs spectral analysis at a fraction of the power used by a digital counterpart. Furthermore, we show that the front-end can be combined with existing sensor nodes to 1) selectively wake up the mote based upon spectral content of the signal, thus increasing battery life without missing interesting events, and to 2) achieve low-power signal analysis using an analog spectral decomposition block, freeing up digital computation resources for higher-level analysis. Experiments in the context of vehicle classification show improved performance for our ASP-interfaced mote over an all-digital implementation.

Hibernets: Energy-Efficient Sensor Networks Using Analog Signal Processing

In this paper, we propose a real-time noise suppression system implemented with analog VLSI. The algorithm implemented is designed to reduce stationary background noise while preserving the non-stationary signal component. Because the system relies on analog computation rather than digital, it has benefits such as extremely low power consumption and real-time computation. The algorithm is based on digital signal processing foundations that are slightly adjusted for use in the continuous-time domain. The analog components described as part of this system include a C/sup 4/ second-order section bandpass filter, peak and minimum detectors, a translinear division circuit, and a differential multiplier. Floating-gate circuits are used to set bias points and adjust offsets.

http://www.hpl.hp.com/techreports/2002/HPL-2002-311.pdf

A continuous-time speech enhancement front-end for microphone inputs

One preferred embodiment of the present invention provides systems and methods for removing noise from an input analog signal in continues time. Briefly described in architecture, one embodiment of the system, among others, can be implemented as follows. An analog filtering system including VLSI circuitry separates an analog input signal into a plurality of sub-band signals. Then, an analog gain system including VLSI circuitry calculates a gain for each sub-band signal that suppresses the noise within the sub-band signal. In some preferred embodiments, VLSI circuitry includes floating gate technology. Methods and other systems are also provided.

David W. Graham

Papers

Indirect Programming of Floating-Gate Transistors

A Low-Power Programmable Bandpass Filter Section for Higher Order Filter Applications

Hibernets: Energy-Efficient Sensor Networks Using Analog Signal Processing

A continuous-time speech enhancement front-end for microphone inputs

Analog audio signal enhancement system using a noise suppression algorithm