Stochastic circuits for real-time image-processing applications

Alaghi, Armin; Li, Cheng; Hayes, John P.

doi:10.1145/2463209.2488901

Cited by 213 publications

(115 citation statements)

References 18 publications

(44 reference statements)

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“…Recently, it has been shown that image edge detection can be performed using stochastic circuits [23,24] very efficiently. To this end, in [23,24] the authors have simulated custom implementation of these algorithms in hardware. Table 5 compares our results with these implementations.…”

Section: Simulation Resultsmentioning

confidence: 99%

Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Pajouhi

Roy

2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers to solve a maze in hardware. In this paper, we utilize swarm intelligence of memristive networks to perform image edge detection. First, we propose a hardware-friendly algorithm for image edge detection based on ant colony optimization. Second, we implement the image edge detection algorithm using memristive networks. Furthermore, we explain the impact of various parameters of the memristors on the efficacy of the implementation. Our results show 28% improvement in the energy compared to a low power CMOS hardware implementation based on stochastic circuits. Furthermore, our design occupies up to 5x less area.

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Section: Simulation Resultsmentioning

confidence: 99%

Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Pajouhi

Roy

2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…In this work, an edge detection circuit has been implemented using the stochastic circuit described in [5] as shown in Figure 1g. This circuit is implemented based on Robert's cross algorithm.…”

Section: Edge Detectionmentioning

confidence: 99%

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing

Seva

Metku

Choi

2017

JLPEA

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Abstract:The high performance of FPGA (Field Programmable Gate Array) in image processing applications is justified by its flexible reconfigurability, its inherent parallel nature and the availability of a large amount of internal memories. Lately, the Stochastic Computing (SC) paradigm has been found to be significantly advantageous in certain application domains including image processing because of its lower hardware complexity and power consumption. However, its viability is deemed to be limited due to its serial bitstream processing and excessive run-time requirement for convergence. To address these issues, a novel approach is proposed in this work where an energy-efficient implementation of SC is accomplished by introducing fast-converging Quasi-Stochastic Number Generators (QSNGs) and parallel stochastic bitstream processing, which are well suited to leverage FPGA's reconfigurability and abundant internal memory resources. the proposed approach has been tested on the Virtex-4 FPGA, and results have been compared with the serial and parallel implementations of conventional stochastic computation using the well-known SC edge detection and multiplication circuits. Results prove that by using this approach, execution time, as well as the power consumption are decreased by a factor of 3.5 and 4.5 for the edge detection circuit and multiplication circuit, respectively.

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“…The magnitude of the gradients, f in (7) using SC is generally derived as the following in (8). Take note that each image pixel, Zn with precision level of 8-bits is coded in SC-bipolar format P n = P(Z n = 1).…”

Section: The Proposed Stochastic Sobel Edge Detectionmentioning

confidence: 99%

A New Lightweight and High Fault Tolerance Sobel Edge Detection Using Stochastic Computing

Wong¹,

Wong²,

Zhang³

et al. 2017

IJCEE

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Abstract:A new Stochastic Computing (SC) circuit design paradigm for image processing system is presented in this work. Two improved SC computational functions are derived, which are namely the stochastic scaled addition and stochastic absolute value of difference. Data correlation is also incorporated in the design for effective circuit size reduction without imposing accuracy degradation in the hardware implementations. The proposed SC functions are next employed to design the new and lightweight Sobel edge detection. Experimental results obtained from detailed test analysis have proven that new implementation has satisfactory accuracy level and higher fault tolerance capability in comparison with their conventional counterparts. The works proposed are also implemented on an Altera Cyclone V 5CGXFC7D6F31C6 FPGA for hardware complexity evaluation.

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Stochastic circuits for real-time image-processing applications

Cited by 213 publications

References 18 publications

Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing

A New Lightweight and High Fault Tolerance Sobel Edge Detection Using Stochastic Computing

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