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

Lotrič, Uroš; Pilipović, Ratko; Bulić, Patricio

doi:10.3390/electronics10101175

Cited by 14 publications

(3 citation statements)

References 55 publications

(111 reference statements)

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“…Ref. [17] proposed an approximate multiplier that uses a mixed radix-4 and logarithmic encoding of the input operands to generate two partial products. It uses exact radix-4 encoding to generate partial products from the three most significant bits.…”

Section: Related Workmentioning

confidence: 99%

A Piecewise Linear Mitchell Algorithm-Based Approximate Multiplier

et al. 2022

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In the field of integrated circuits, the computational cost has always been a crucial design metric. In recent years, with the continuous development in the field of computing, the requirements for computation have been growing rapidly. Reducing the computational cost and improving computational efficiency have become the key issues in the field. There are many error-tolerant applications in the multimedia field where approximate computing techniques can be applied to improve computational efficiency and reduce computational costs at the cost of acceptable computational errors. This paper proposed a piecewise linear Mitchell algorithm based on Mitchell logarithmic approximation multiplication algorithm. Additionally, the Pwl-Mit multiplier is designed according to the improved algorithm combined with the data truncation technique. The proposed approximate multiplier has better statistical performance compared with the state-of-the-art multipliers. The design is simulated and synthesized at the TSMC 65 nm process, and its reliability is verified using discrete cosine transform (DCT) transform.

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Section: Related Workmentioning

confidence: 99%

A Piecewise Linear Mitchell Algorithm-Based Approximate Multiplier

et al. 2022

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“…5 as a starting point for further simplification. The first step is to substitute the XOR gate of the half adder in column 5, with an OR gate: (9) The carry of the same half adder is: (10) The sum of the last half adder is: (11) By neglecting the second term: (12) With a final approximation: (13) The carry of the last Half Adder is: (14) The resulting design is named N2 and shown in Fig. 6.…”

Section: ×4 Approximate Multiplier N2mentioning

confidence: 99%

“…Several techniques providing efficient approximate multipliers have been studied in the literature. One such example is the approximate logarithmic multiplier [10]- [12]. In this case, approximated versions of the logarithms of the input operands, are added.…”

Section: Introductionmentioning

confidence: 99%

Approximate Recursive Multipliers Using Low Power Building Blocks

Zacharelos

Nunziata

Saggese

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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Approximate computing, frequently used in error tolerant applications, aims to achieve higher circuit performances by allowing the possibility of inaccurate results, rather than guaranteeing a correct outcome. Many contributions target the binary multiplier aiming to minimize the complexity of this common yet power-hungry circuit. Approximate recursive multipliers are low-power designs that exploit approximate building blocks to scale up to their final size. In this paper, we present two novel 4x4 approximate multipliers obtained by carry manipulation. They are used to compose 8x8 designs with different error-precision trade-off. The final circuits exhibit a competitive behavior in terms of error while reducing the power dissipation when compared to state-of-the-art proposals. The proposed multipliers and state-of-the-art designs found in the literature, have been synthesized targeting a 14nm FinFET technology to determine the electrical characteristics. Compared with an exact 8×8 multiplier, the least dissipative design proposed in this paper reduces power consumption and silicon area by 46%, and minimum delay by 21%. It also consumes 14% less power than the least power-hungry recursive circuit found in the literature, while offering 81% higher accuracy. Ιmage processing applications and a convolutional neural network are shown to demonstrate the effectiveness of the proposed multipliers.

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