Approximate stochastic computing (ASC) for image processing applications

Seva, Ramu; Metku, Prashanthi; Kim, Kyung Ki; Kim, Yong-Bin; Choi, Minsu

doi:10.1109/isocc.2016.7799758

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…SC is a non-traditional computing technology that encodes information with finitelength stochastic sequences of '0's and '1's. The probabilistic elements enable the simplification of complex operations and resilience to errors, and various research projects utilizing these properties have been carried out [7,9,[23][24][25][26][27][28]. Particularly, studies have been conducted to enhance the performance of SC and apply it to applications requiring robustness against noise or demanding low power consumption, such as artificial intelligence filtering operations [7,23,24] and image processing [9,[25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

“…These were validated by applying adders and multipliers to the edge detection algorithm, resulting in a reduction in area utilization (64%) and power consumption (96%) compared to the accurate edge detection. In [26], a new technique called approximate stochastic computing (ASC) for improving computation time in image processing was proposed. The research verified its effect on computation time by an edge detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In [26], the authors propose a new technique called the approximate stochastic computing (ASC) approach focusing on image processing applications. This approach reduces the computation time of an SC by a factor of 16 at a trade-off of an error percentage of 3.13% in the absolute stochastic value.…”

Section: Related Workmentioning

confidence: 99%

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Lightweight and Error-Tolerant Stereo Matching with a Stochastic Computing Processor

An,

Oh,

Lee

et al. 2024

Electronics

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Stereo matching, utilized in diverse fields, poses a challenge to systems in resource-constrained environments due to the significant growth of computational load with image resolution. The challenge is crucial for the systems because fields utilizing stereo matching require short operational time for real-time applications and low power architecture. Stochastic computing (SC) is able to be a valuable approach to address the challenge by reducing the computational load by representing binary numbers with stochastic sequences, which are encoded as a probability value, and by leveraging the concept of mathematical probability. Also, it is possible for a system to be error-tolerant by utilizing the characteristics of stochastic computing. Therefore, in this paper, we propose an approach for lightweight and error-tolerant stereo matching with a hardware-implemented stochastic computing processor. To verify the feasibility and error tolerance of the proposed system, we implemented the proposed system and conducted experiments comparing depth maps with or without stochastic computing by calculating similarities. According to the experimental results, the proposed system indicated no significant differences in output depth maps and achieved an improvement in the depth maps from error-injected input images by an average of 58.95%. Therefore, we demonstrated that stereo matching with stochastic computing is feasible and error-tolerant.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Lightweight and Error-Tolerant Stereo Matching with a Stochastic Computing Processor

An,

Oh,

Lee

et al. 2024

Electronics

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“…Image processing on a pixel basis is relatively simple, but the overall computation of image processing is very large because of the large number of pixels. The SC enables repetitive image computation circuits at a low cost through the simplification of the circuits [24]. Image processing with SC is implemented through various algorithms such as edge detection [15] and Gamma Correction [25] and applied to complex systems such as autonomous driving [26].…”

Section: Image Processingmentioning

confidence: 99%

Parallel Stochastic Computing Architecture for Computationally Intensive Applications

Kim

Jeong

et al. 2023

Electronics

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Stochastic computing requires random number generators to generate stochastic sequences that represent probability values. In the case of an 8-bit operation, a 256-bit length of a stochastic sequence is required, which results in latency issues. In this paper, a stochastic computing architecture is proposed to address the latency issue by employing parallel linear feedback shift registers (LFSRs). The proposed architecture reduces the latency in the stochastic sequence generation process without losing accuracy. In addition, the proposed architecture achieves area efficiency by reducing 69% of flip-flops and 70.4% of LUTs compared to architecture employing shared LFSRs, and 74% of flip-flops and 58% of LUTs compared to the architecture applying multiple LFSRs with the same computational time.

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“…The processes comprise three major steps: image analysis, tracking as well as segmentation. Further, enhanced modalities are also implemented for the improvement of computational efficiency of image processing applications [4–13].…”

Section: Introductionmentioning

confidence: 99%