Stochastic computing

Gaines, Brian R.

doi:10.1145/1465482.1465505

Cited by 220 publications

(90 citation statements)

References 4 publications

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“…Hence, as shown in the first column of Table 1, is chosen from the following candidates: {1}, {2}, and {1, 2}. For each candidate of , we get all the corresponding 1,2 and see whether criterion (b) is satisfied. If it is, the candidate is valid.…”

Section: Requriementioning

confidence: 99%

“…Stochastic computation, first introduced in 1960s [1], has been widely used in many fields: neural network [2,3], digital image processing [4][5][6][7], channel decoding [8][9][10], MIMO detection [11,12], reliability evaluation [13], and so on. The main idea of stochastic computation is to represent continuous values with stochastic bit streams, which makes it possible to perform complex arithmetic computations with simple bitwise operations.…”

Section: Introductionmentioning

confidence: 99%

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Random Error Reduction Scheme for Combinational Stochastic Circuit

Ye¹,

Hu²

2017

Mathematical Problems in Engineering

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In conventional stochastic computation, all the input streams are Bernoulli sequences (BSs), which may result in large random error. To reduce random error and improve computational accuracy, some other sequences have been reported as alternatives to BSs. However, these sequences only apply to the specific stochastic circuits, have difficulties in hardware generation, or have length constraints. To this end, new sequences without these disadvantages should be considered. This paper proposes the random error analysis method for stochastic computation based on autocorrelation sequence (AS), which is more general than the conventional one based on BS. The analysis results show that we can use the proper ASs as input streams of stochastic circuits to reduce random error. On the basis of that conclusion, we propose the random error reduction scheme based on maximal concentrated autocorrelation sequence (MCAS) and BS, both of which are ASs. MCAS and BS are applicable to any combinational stochastic circuit, are easily generated by hardware, and have no length constraints, which avoid the disadvantages of sequences in the previous work. Moreover, we apply the proposed random error reduction scheme into several typical stochastic circuits as case studies. The simulation results confirm the effectiveness of the proposed scheme.

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Section: Requriementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random Error Reduction Scheme for Combinational Stochastic Circuit

Ye¹,

Hu²

2017

Mathematical Problems in Engineering

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“…SC has its roots in the 1960s, and it is used for probability representation using digital bit streams [3,4]. SC has been successfully applied to many applications like image processing, neural networks, LDPCcodes, factor graphs, fault-tree analysis and in filters [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Implementation Of Reconfigurability Number Generators For Digital Bit Stream Signals

2018

IJRTER

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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 bit stream processing and excessive runtime 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 bit stream 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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“…Stochastic computing (SC) [1] was first introduced several decades ago and it has recently gained a fair amount of attentions in the integrated circuit (IC) design community. This technique, which incorporates elements from probability theory, has proven to be able to handle computation uncertainties in a more effective and efficient manner [2].…”

Section: Introductionmentioning

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 computing

Cited by 220 publications

References 4 publications

Random Error Reduction Scheme for Combinational Stochastic Circuit

Random Error Reduction Scheme for Combinational Stochastic Circuit

Implementation Of Reconfigurability Number Generators For Digital Bit Stream Signals

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

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