Design of Approximate Logarithmic Multipliers

Liu, Weiqiang; Xu, Jiahua; Wang, Danye; Lombardi, Fabrizio

doi:10.1145/3060403.3060409

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…• Approximate Arithmetic Circuits: simplify circuit designs to achieve an approximate operation of the desired function, such as addition, multiplication and division. The main approximate arithmetic units include approximate adder [32], [33], approximate multipliers [34], [35], [36] and approximate dividers [37] that have been proposed. Other approximate circuits have approximate fast fourier transform (FFT) [38] and approximate CORDIC [39].…”

Section: A Design Objectivesmentioning

confidence: 99%

“…Therefore, nondeterministic approximate designs have limited reproducibility. The examples of deterministic approximate computing techniques in the above mentioned publications are [14], [16], [18], [15], [17], [19], [23], [24], [29], [30], [32], [33], [34], [35], [36], [34], [35], [36]. The non-deterministic approximate computing techniques of the above mentioned research include [20], [21], [25], [26], [27], [28], [40], [41].…”

Section: A Design Objectivesmentioning

confidence: 99%

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Security in Approximate Computing and Approximate Computing for Security: Challenges and Opportunities

Liu

O’Neill

et al. 2020

Proc. IEEE

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Approximate computing, an advanced computational technique which returns inaccurate but acceptable results instead of exact results, has emerged as a new preferable paradigm over traditional computing architectures for energy efficient system designs. It is crucial for nanoscale integrated circuits (ICs) to achieve high speed and low power, where some intrinsic errors are acceptable, such as (deep-) machine learning, image processing, communication and other error-tolerant and cognitive applications. However, approximate computing also introduces security vulnerabilities mainly due to the uncertainty and unpredictability of intrinsic errors during approximate execution which may be indistinguishable using malicious modification of the accurate result. On the other hand, interestingly, approximate computing can also provide new approaches for security. Existing literature in approximate computing covers threat models, countermeasures, and evaluations, but lacks a framework for analysis and comparison. In this paper, we provide a classification of the state of the art in this research field, including threat models in approximate computing and promising security approaches using approximate computing. Index Terms-Approximate computing, hardware security, cryptography I. INTRODUCTION N the last decade, various advanced computing systems, including supercomputers, ubiquitous computing centers, and servers, have been developed and widely deployed. Unfortunately, Moore's law is approaching its limitation [1], and conventional computing techniques are not able to provide higher computing performance under the restriction of power consumption. Therefore, new nanoscale computing paradigms are urgently required for low power and high performance computing systems. Hence, appropriate reduction of the computational accuracy could effectively improve the performance of computing systems without sacrificing functionality and perception.

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Section: A Design Objectivesmentioning

confidence: 99%

Section: A Design Objectivesmentioning

confidence: 99%

Security in Approximate Computing and Approximate Computing for Security: Challenges and Opportunities

Liu

O’Neill

et al. 2020

Proc. IEEE

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this section the proposed multipliers are compared with ten other multipliers: truncated1 [7], truncated2 [8], truncated3 [9], LOA [5], Momeni [15], Ma [16], Liu [13,14], Kulkani [11], Accurate3:2 (the Dadda multiplier constructed by 3:2 precise compressors), and Accurate4:2 (the Dadda multiplier constructed by 4:2 precise compressors). Table 4 shows the MSE, the delay, the number of transistors, and the product of delay and the number of transistors (PDT) of each multiplier.…”

Section: Simulationsmentioning

confidence: 99%

“…In [12], an approximate signed multiplier was proposed which is 20% faster than a precise signed multiplier. In [13,14], in order to reduce the carry propagation delay, an approximate adder was proposed that compute i-th bit of the summation of the two number A and B, i.e. Si, based on only the i-th and (i-1)-th bit of the two numbers.…”

Section: Introductionmentioning

confidence: 99%