Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to conventional binary computing. It is unique in that it represents and processes information in the form of digitized probabilities. SC employs very low-complexity arithmetic units which was a primary design concern in the past. Despite this advantage and also its inherent error tolerance, SC was seen as impractical because of very long computation times and relatively low accuracy. However, current technology trends tend to increase uncertainty in circuit behavior and imply a need to better understand, and perhaps exploit, probability in computation. This article surveys SC from a modern perspective where the small size, error resilience, and probabilistic features of SC may compete successfully with conventional methodologies in certain applications. First, we survey the literature and review the key concepts of stochastic number representation and circuit structure. We then describe the design of SC-based circuits and evaluate their advantages and disadvantages. Finally, we give examples of the potential applications of SC and discuss some practical problems that are yet to be solved.

/pdf/survey-of-stochastic-computing-10orkcfmr8.pdf

Survey of Stochastic Computing

A CMOS type semiconductor image sensor module wherein a pixel aperture ratio is improved, chip use efficiency is improved and furthermore, simultaneous shutter operation by all the pixels is made possible, and a method for manufacturing such semiconductor image sensor module are provided. The semiconductor image sensor module is provided by stacking a first semiconductor chip, which has an image sensor wherein a plurality of pixels composed of a photoelectric conversion element and a transistor are arranged, and a second semiconductor chip, which has an A/D converter array. Preferably, the semiconductor image sensor module is provided by stacking a third semiconductor chip having a memory element array. Furthermore, the semiconductor image sensor module is provided by stacking the first semiconductor chip having the image sensor and a fourth semiconductor chip having an analog nonvolatile memory array.

Semiconductor image sensor module and method of manufacturing the same

Real-time image-processing applications impose severe design constraints in terms of area and power. Examples of interest include retinal implants for vision restoration and on-the-fly feature extraction. This work addresses the design of image-processing circuits using stochastic computing techniques. We show how stochastic circuits can be integrated at the pixel level with image sensors, thus supporting efficient real-time (pre)processing of images. We present the design of several representative circuits, which demonstrate that stochastic designs can be significantly smaller, faster, more power-efficient, and more noise-tolerant than conventional ones. Furthermore, the stochastic designs naturally produce images with progressive quality improvement.

/pdf/stochastic-circuits-for-real-time-image-processing-56uhvmw0xd.pdf

Stochastic circuits for real-time image-processing applications

A two-terminal memristor device is a promising digital memory for its high integration density, substantially lower energy consumption compared to CMOS, and scalability below 10 nm. However, a nanoscale memristor is an inherently stochastic device, and extra energy and latency are required to make a deterministic memory based on memristors. Instead of enforcing deterministic storage, we take advantage of the nondeterministic memory for native stochastic computing, where the randomness required by stochastic computing is intrinsic to the devices without resorting to expensive stochastic number generation. This native stochastic computing system can be implemented as a hybrid integration of memristor memory and simple CMOS stochastic computing circuits. We use an approach called group write to program memristor memory cells in arrays to generate random bit streams for stochastic computing. Three methods are proposed to program memristors using stochastic bit streams and compensate for the nonlinear memristor write function: voltage predistortion, parallel single-pulse write, and downscaled write and upscaled read. To evaluate these technical approaches, we show by simulation a memristor-based stochastic processor for gradient descent optimization, and k-means clustering. The native stochastic computing based on memristors demonstrates key advantages in energy and speed in compute-intensive, data-intensive, and probabilistic applications.

A Native Stochastic Computing Architecture Enabled by Memristors

The claimed subject matter provides a system and/or method that facilitates providing real-time in situ measurements in an environment. A sensor module can employ real-time in situ measurements associated with a parameter within the environment. A sensor operation module communicatively coupled to the sensor module via an interface can analyze the real-time in situ measurements, wherein at least one of the sensor module and the sensor operation module allows expansion for at least one module.

Modular, configurable, intelligent sensor system

Aspects of a method and system for change detection in localization and tracking of objects in a smart video camera are provided. A programmable surveillance video camera comprises processors for detecting objects in a video signal based on an object mask. The processors may generate a textual representation of the video signal by utilizing a description language to indicate characteristics of the detected objects, such as shape, texture, color, and/or motion, for example. The object mask may be based on a detection field value generated for each pixel in the video signal by comparing a first observation field and a second observation field associated with each of the pixels. The first observation field may be based on a difference between an input video signal value and an estimated background value while the second observation field may be based on a temporal difference between first observation fields.

Method and system for background estimation in localization and tracking of objects in a smart video camera

A method and an image sensor circuit ( 2 ) for providing a bit stream pulse modulated signal at a controlled bit rate. The image sensor circuit ( 2 ) has a two-dimensional array of pixel signal pulse frequency modulators ( 22 ) each having a photodiode ( 32 ) for converting incident light into a varying signal voltage and a frequency-to-digital converter which generates a pulse frequency modulated signal from the varying signal voltage. The circuit 2 also has a sampling unit ( 24 ) which samples the pulse frequency modulated signal to generate the bit stream pulse modulated signal at said controlled bit rate. An arithmetic unit ( 26 ) is coupled to an output of the sampling unit ( 24 ) for performing stochastic processing on the bit stream pulse modulated signal.

Image sensor circuit and method

Aspects of a method and system for processing video data are disclosed and may include detecting, within a single chip in a programmable surveillance video camera, one or more moving objects in a raw video signal generated by the programmable surveillance video camera. One or more characteristics of the detected one or more objects may be extracted within the single chip in the programmable surveillance video camera. The extraction may be based on the raw video signal and may be performed prior to compression of the raw video data. The characteristics of the detected one or more objects may include shape, texture, color, motion presence, motion direction, sequence name, location, links, and/or alarm type. One or more textual representations of at least one of the characteristics of the detected one or more objects may be generated within the single chip in the programmable surveillance video camera.

Smart network camera system-on-a-chip

A method of providing range measurements for use with a vehicle, the method comprising the steps of: a) visually sensing (2) the area adjacent the vehicle to produce visual sense data (22); b) range sensing (26) objects around the vehicle to produce range sense data; c) combining the visual sense data and the range sense data to produce, with respect to the vehicle, an estimate of ranges to the objects around the vehicle (28). The estimate of ranges to the objects around the vehicle may be displayed (29) to a driver.

Integrated vehicular system for low speed collision avoidance

This paper describes a method for improving image quality in a color CMOS image sensor. The technique simultaneously acts to compress the dynamic range, reorganize the signal to improve visibility, suppress noise, identify local features, achieve color constancy, and lightness rendition. An efficient hardware architecture and a rigorous analysis of the different modules are presented to achieve high quality CMOS digital cameras.

/pdf/novel-image-enhancement-technique-using-shunting-inhibitory-1scfzekfor.pdf

Novel image enhancement technique using shunting inhibitory cellular neural networks

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