An Ultra-Low Power CMOS Image Sensor with On-Chip Energy Harvesting and Power Management Capability

Cevik, Ismail; Huang, Xiwei; Yu, Hao; Yan, Mei; Ay, Suat U.

doi:10.3390/s150305531

Cited by 43 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study obtained the fastest processing speed of up to 120 fps while maintaining low power consumption, 1.12 mW. Furthermore, the power consumption is a function of the frame rate, which is about 15%-44% of the power reduction with a 10× decrease in the frame rate [32,33]. With the low frame rate, the estimated total power consumption of the proposed CIS is 0.16 mW with 1 fps.…”

Section: Classification Resultsmentioning

confidence: 82%

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

Choi

Lee

Son

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 μm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 × 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120.

show abstract

Section: Classification Resultsmentioning

confidence: 82%

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

Choi

Lee

Son

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, to evaluate the energy consumption of the proposed PAADA, we suppose that 6.53uW power is consumed for sampling an image by an CMOS image sensor which is widely used in imaging system [46]. Then the total energy consumed is the summation of energy consumption of multiple sampling times.…”

Section: ) Performance Metricmentioning

confidence: 99%

PAADA: Physical-World-Aware Approximate Data Acquisition for Image Sensor Networks

et al. 2020

IEEE Access

View full text Add to dashboard Cite

To observe the complicated physical world, sensor networks are widely used for data collection. Moreover, due to the limited energy, storage and computation capacity of sensor nodes, approximate data acquisition in adaptive sampling manner is a wide choice. Nevertheless, existing data acquisition methods are most designed for univariate data (e.g., temperature), and thus not applicable to image data with high dimensions and complex structures. In this paper, we propose a framework of Physical-world-Aware Adaptive Data Acquisition (PAADA) for image sensor networks, to sample data adaptively with prespecified error bound. First, based on the convolutional autoencoders (CAEs), PAADA compresses the highdimensional image data into a feature vector with a handful of hidden variables which compactly capture the key features of the image data. Second, PAADA designs a Physical-world-aware Adaptive Sampling (PAS) algorithm based on the Hermitee interpolation. Under the feature space, the PAS algorithm adjusts the sampling frequency automatically by considering the change trend of the feature vector. In addition, the feature vectors at non-sampling time points can be recovered with O () approximation guarantee to the ground truths. Next, PAADA recovers the image data at non-sampling time points based on the recovered feature vectors. Finally, for each sensor, PAADA returns an image series composed of sampled images (at sampling time points) and approximate images (at non-sampling time points). Experiments on realworld datasets demonstrate that the proposed PAADA has high performance in both accuracy and energy consumption. INDEX TERMS Image sensor networks, adaptive data acquisition, physical-world-aware approximation.

show abstract

“…There is an inherent trade-off between the generation of big data by such imaging systems, and efficiency in extraction of useful information within real-time constraints, limiting the efficacy of such sensors in real-time decision-making systems [10,11]. The traditional imaging system gets burdened by the acquisition, transmission, and storage of excess data, bearing redundant information for the given application of interest [12][13][14][15][16]. Transmission of the extra information requires a high bandwidth and consumption of extra power to store or transmit.…”

Section: Introductionmentioning

confidence: 99%

CMOS approach to compressed-domain image acquisition

Ghasemi¹,

Bhattarai²,

Fiorante³

et al. 2017

Opt. Express

View full text Add to dashboard Cite

A hardware implementation of a real-time compressed-domain image acquisition system is demonstrated. The system performs front-end computational imaging, whereby the inner product between an image and an arbitrarily-specified mask is implemented in silicon. The acquisition system is based on an intelligent readout integrated circuit (iROIC) that is capable of providing independent bias voltages to individual detectors, which enables implementation of spatial multiplication with any prescribed mask through a bias-controlled response-modulation mechanism. The modulated pixels are summed up in the image grabber to generate the compressed samples, namely aperture-coded coefficients, of an image. A rigorous bias-selection algorithm is presented to the readout circuit, which exploits the bias-dependent nature of the imager's responsivity. Proven functionality of the hardware in transform coding compressed image acquisition, silicon-level compressive sampling, in pixel nonuniformity correction and hardware-level implementation of region-based enhancement is demonstrated.

show abstract

An Ultra-Low Power CMOS Image Sensor with On-Chip Energy Harvesting and Power Management Capability

Cited by 43 publications

References 29 publications

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

PAADA: Physical-World-Aware Approximate Data Acquisition for Image Sensor Networks

CMOS approach to compressed-domain image acquisition

Contact Info

Product

Resources

About