Systems And Methods For Providing Programmable Analog Classifiers

The present invention describes systems and methods to provide programmable analog classifiers. An exemplary embodiment of the present invention provides an analog classifier circuit comprising a bump circuit enabled to store a template vector, wherein the template vector can model a probability distribution with exponential behavior. Furthermore, the bump circuit is enabled to generate an output corresponding to a comparison between an input vector received by the bump circuit and the template vector stored by the bump circuit. Additionally, the analog classifier circuit includes a variable gain amplifier in communication with the bump circuit, and the variable gain amplifier can be adjusted to modify the variance of the template vector.Georgia Tech Research Corporatio

An Analog VLSI Deep Machine Learning Implementation

Machine learning systems provide automated data processing and see a wide range of applications. Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog signal processing (ASP) can yield much higher energy efficiency than digital signal processing (DSP), presenting means of overcoming these limitations. The purpose of this work is to develop an analog implementation of DML system. First, an analog memory is proposed as an essential component of the learning systems. It uses the charge trapped on the floating gate to store analog value in a non-volatile way. The memory is compatible with standard digital CMOS process and allows random-accessible bi-directional updates without the need for on-chip charge pump or high voltage switch. Second, architecture and circuits are developed to realize an online k-means clustering algorithm in analog signal processing. It achieves automatic recognition of underlying data pattern and online extraction of data statistical parameters. This unsupervised learning system constitutes the computation node in the deep machine learning hierarchy. Third, a 3-layer, 7-node analog deep machine learning engine is designed featuring online unsupervised trainability and non-volatile floating-gate analog storage. It utilizes massively parallel reconfigurable current-mode analog architecture to realize efficient computation. And algorithm-level feedback is leveraged to provide robustness to circuit imperfections in analog signal processing. At a processing speed of 8300 input vectors per second, it achieves 1×1012 operation per second per Watt of peak energy efficiency. In addition, an ultra-low-power tunable bump circuit is presented to provide similarity measures in analog signal processing. It incorporates a novel wide-input-range tunable pseudo-differential transconductor. The circuit demonstrates tunability of bump center, width and height with a power consumption significantly lower than previous works

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

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

Analog Signal Processing Elements for Energy-Constrained Platforms

Energy constrained processing poses a number of challenges that have resulted in tremendous innovations over the past decade. Shrinking supply voltages and limited clock speeds have placed an emphasis on processing efficiency over the raw throughput of a processor. One of the approaches to increase processing efficiency is to use parallel processing with slower, lower resolution processing elements. By utilizing this parallel approach, power consumption can be decreased while maintaining data throughput relative to other more power-hungry architectures.;This low resolution / parallel architecture has direct application in the analog as well as the digital domain. Indeed, research shows that as the resolution of a signal processor falls below a system-dependent threshold, it is almost always more efficient to preform the processing in the analog domain. These continuous-time circuits have long been used in the most energy-constrained applications, ranging from pacemakers and cochlear implants to wireless sensor motes designed to run autonomously for months in the field.;Most audio processing techniques utilize spectral decomposition as the first step of their algorithms, whether by a FFT/DFT in the digital domain or a bank of bandpass filters in the analog domain. The work presented here is designed to function within the parallel, array-based environment of a bank of bandpass filters. Work to improve the simulation of programmable analog storage elements (Floating-Gate transistors) in typical SPICE-based simulators is presented, along with a novel method of harnessing the unique properties of these Floating-Gate (FG) transistors to extend the linear range of a differential pair. These improvements in simulation and linearity are demonstrated in a Variable-Gain Amplfier (VGA) to compress large differential inputs into small single-ended outputs suitable for processing by other analog elements. Finally, a novel circuit composed of only six transistors is proposed to compute the continuous-time derivative of a signal within the sub-banded architecture of the bandpass filter bank

A Monolithic Gm-C Filter based Very Low Power, Programmable, and Multi-Channel Harmonic Discrimination System using Analog Signal Processing

A highly selective monolithic band-pass filter with programmable characteristics at micro-power operation is presented. Very low power signal processing is of great interest in wireless sensing and Internet-of-Things applications. This filter enables long-term battery powered operation of a highly selective harmonic signal discriminator for an analog signal processing system. The Gm-C biquadratic circuits were fabricated in a 0.18-μm [micrometer] CMOS process. Each 2nd-order biquad filter nominally consumes 20 μW [microwatt] and can be programmed for the desired gain (0db3dB), quality factor (5 to 20), and center-frequency from 1kHz to 100kHz. The 8th-order filter channel achieved an effective quality factor of 30 at 100kHz with an overall power consumption of 108 μW