Using Approximate Computing and Selective Hardening for the Reduction of Overheads in the Design of Radiation-Induced Fault-Tolerant Systems

Aponte-Moreno, Alexander; Restrepo-Calle, Felipe; Pedraza, César

doi:10.3390/electronics8121539

Cited by 2 publications

(3 citation statements)

References 51 publications

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“…we provide a mean value approximation of the removed bits. Introducing both approximations to (10), we get…”

Section: The Binary-to-logarithm Conversion Stagementioning

confidence: 99%

“…Approximate computing has emerged as a new paradigm for energy-efficient systems where an acceptable error is induced in computing to achieve more efficient processing [1][2][3][4][5][6]. For example, approximate computing has been used at different system levels [7][8][9][10][11][12][13][14][15][16][17][18] and various approximate arithmetic circuits have been designed to save chip area and energy [8,11,[19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Radix-4 and Approximate Logarithmic Multiplier for Energy Efficient Image Processing

2021

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Multiplication is an essential image processing operation commonly implemented in hardware DSP cores. To improve DSP cores’ area, speed, or energy efficiency, we can approximate multiplication. We present an approximate multiplier that generates two partial products using hybrid radix-4 and logarithmic encoding of the input operands. It uses the exact radix-4 encoding to generate the partial product from the three most significant bits and the logarithmic approximation with mantissa trimming to approximate the partial product from the remaining least-significant bits. The proposed multiplier fills the gap between highly accurate approximate non-logarithmic multipliers with a complex design and less accurate approximate logarithmic multipliers with a more straightforward design. We evaluated the multiplier’s efficiency in terms of error, energy (power-delay-product) and area utilisation using NanGate 45 nm. The experimental results show that the proposed multiplier exhibits good area utilisation and energy consumption and behaves well in image processing applications.

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“…we provide a mean value approximation of the removed bits. Introducing both approximations to (10), we get…”

Section: The Binary-to-logarithm Conversion Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Radix-4 and Approximate Logarithmic Multiplier for Energy Efficient Image Processing

2021

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“…Approximate computing is a new paradigm where an acceptable error is induced in the computing to achieve more energy-efficient processing [ 28 , 29 , 30 , 31 , 32 , 33 ]. It has been introduced at different system levels [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], and a large number of approximate arithmetic circuits have been designed to save chip area and energy [ 35 , 38 , 46 , 47 , 48 , 49 , 50 , 51 ]. Multiplication is a very common, but expensive operation, with exact multipliers being large circuits that consume a significant amount of energy.…”

Section: Introductionmentioning

confidence: 99%

An Approximate GEMM Unit for Energy-Efficient Object Detection

Pilipović

Risojević

Božič

et al. 2021

Sensors

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Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is to achieve the best tradeoff between design efficiency and accuracy. The essential operation in artificial intelligence algorithms is the general matrix multiplication (GEMM) operation comprised of matrix multiplication and accumulation. This paper presents an approximate general matrix multiplication (AGEMM) unit that employs approximate multipliers to perform matrix–matrix operations on four-by-four matrices given in sixteen-bit signed fixed-point format. The synthesis of the proposed AGEMM unit to the 45 nm Nangate Open Cell Library revealed that it consumed only up to 36% of the area and 25% of the energy required by the exact general matrix multiplication unit. The AGEMM unit is ideally suited to convolutional neural networks, which can adapt to the error induced in the computation. We evaluated the AGEMM units’ usability for honeybee detection with the YOLOv4-tiny convolutional neural network. The results implied that we can deploy the AGEMM units in convolutional neural networks without noticeable performance degradation. Moreover, the AGEMM unit’s employment can lead to more area- and energy-efficient convolutional neural network processing, which in turn could prolong sensors’ and edge nodes’ autonomy.

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Using Approximate Computing and Selective Hardening for the Reduction of Overheads in the Design of Radiation-Induced Fault-Tolerant Systems

Cited by 2 publications

References 51 publications

A Hybrid Radix-4 and Approximate Logarithmic Multiplier for Energy Efficient Image Processing

A Hybrid Radix-4 and Approximate Logarithmic Multiplier for Energy Efficient Image Processing

An Approximate GEMM Unit for Energy-Efficient Object Detection

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