ABACUS: A technique for automated behavioral synthesis of approximate computing circuits

Nepal, Kumud; Li, Yueting; Bahar, R. Iris; Reda, Sherief

doi:10.7873/date.2014.374

Cited by 51 publications

(58 citation statements)

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“…The first line of research has devised custom approximate designs for typical arithmetic building blocks (e.g., adders, multipliers [2][3][4]15]). The second line has targeted approximation of more generic circuits either from gate-level (i.e., Boolean descriptions) [7,9,14,17,18], higher-level descriptions, such as RTL or behavioral descriptions [13], or even direct C to approximate hardware synthesis [6].…”

Section: Introductionmentioning

confidence: 99%

Blasys

Hashemi

Tann

Reda

2018

Proceedings of the 55th Annual Design Automation Conference

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Approximate computing is an emerging paradigm where design accuracy can be traded off for benefits in design metrics such as design area, power consumption or circuit complexity. In this work, we present a novel paradigm to synthesize approximate circuits using Boolean matrix factorization (BMF). In our methodology the truth table of a sub-circuit of the design is approximated using BMF to a controllable approximation degree, and the results of the factorization are used to synthesize a less complex subcircuit. To scale our technique to large circuits, we devise a circuit decomposition method and a subcircuit design-space exploration technique to identify the best order for subcircuit approximations. Our method leads to a smooth trade-off between accuracy and full circuit complexity as measured by design area and power consumption. Using an industrial strength design flow, we extensively evaluate our methodology on a number of testcases, where we demonstrate that the proposed methodology can achieve up to 63% in power savings, while introducing an average relative error of 5%. We also compare our work to previous works in Boolean circuit synthesis and demonstrate significant improvements in design metrics for same accuracy targets.

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Section: Introductionmentioning

confidence: 99%

Blasys

Hashemi

Tann

Reda

2018

Proceedings of the 55th Annual Design Automation Conference

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“…However, most methods presented are based on manual interaction. Only a few present an automated approach, like Nepal et al [8] did, who proposed an automated method, based on the behavioral description. Some works focus on approximation of circuits at synthesis time [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Design of fine-grained sequential approximate circuits using probability-aware fault emulation

May

Stechele

2015

2015 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)

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Approximate Computing has recently drawn interest due to its promise to substantially decrease the power consumption of integrated circuits. By tolerating a certain imprecision at a circuit output, the circuit can be operated at a more resource-saving state. For instance, parts of the circuit could be switched off or driven at sub-threshold voltage. Clearly, not all applications are suitable for this approach. Especially applications from the signal and image processing domain are applicable, due to their intrinsic tolerance to imprecision. But even for these circuits, one has to be very careful where to approximate a circuit and to what extent, in order not to fall below a minimum required QoS. In this paper we are presenting an approach to generate approximate circuits from existing deterministic implementations. The flow reaches from application-driven QoS definition down to approximated RTL. We are employing FPGA-based fault emulation of the circuit in order to find out how faults, i.e. imprecisions in the circuit, affect the overall circuit behavior. Most existing approaches only consider combinational circuits. Our proposed methodology is able to approximate complete sequential circuits. Due to the FPGA-based emulation, our approach is very fast and accurate. And furthermore, it allows us to fine-granular tune the resulting precision to the required QoS, in order to get the most out of the approximation.

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“…A further variant is the peak signal-to-noise ratio (PSNR), which considers the ratio between the maximum value assumed by the correct data (max.kv c k) and the noise variance. Lastly, many literature works use higher level application-specific models of error/quality [10,37,40]. Since these metrics are so specific, they are too many to be enumerated here.…”

Section: Composite Metrics and The Fail Small Or Fail Rare Conceptmentioning

confidence: 99%

“…starting from a functional model of the system behavior (typically graph-based) rather than from a net of logic gates. The main effort is documented in [40], where the authors propose a tool named Automated Behavioral Approximate CircUit Synthesis (ABACUS), that produces multiple approximate versions of a circuit starting from an original accurate model in behavioral HDL. ABACUS operates by transforming the HDL in an abstract syntax tree (AST) model, then modifying the AST with a series of operators, each of which introduces some approximations while preserving syntactic correctness.…”

Section: Approximate Behavioral Synthesismentioning

confidence: 99%

“…This makes it more accessible to hardware and software engineers accustomed to standard design flows. As an example, many embodiments of approximate computing are inspired by techniques from the hardware and software reliability literature [7,22,46], while others make use of modified versions of standard electronic design automation (EDA) algorithms [40,47,50].…”

Section: Approximate Computingmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Digital Processing via Approximate Computing

Pagliari

Poncino

Macii

2016

Smart Systems Integration and Simulation

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While the most characteristic feature of a smart system is its capability of sensing a set of environmental quantities and actuating appropriate actions in response to those signals, it is obvious that a significant part of its functional operations is involved with the elaboration of the information carried by the signals [14]. This elaboration is usually done after converting the analog, asynchronous environmental signals into the digital domain.Part of the smartness of a smart system is therefore expressed in the autonomous and transparent operation based on closed loop control and predictive capabilities, as well as improved signal processing technologies. The former functions are normally carried out by a micro-controller or processor core, whereas the latter ones rely on either a digital signal processor (DSP) or an application-specific integrated circuit (ASIC). Hybrid architectures, that combine one or more general purpose CPUs with one or more hardware accelerators are also increasingly popular [11].Such "processing" dimension, coupled with the energy-autonomous nature of these systems put significant emphasis on their energy efficiency [4,12]. Measures for reducing energy (and power) consumption vary according to the engineering domain of the component being considered. In the computing subsystem, classical low-power techniques for processors and digital circuits can be fruitfully exploited [35]. In this chapter, however, we focus on the explicit signal processing task and show how we can effectively leverage an emerging design paradigm called approximate computing [20,52].

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ABACUS: A technique for automated behavioral synthesis of approximate computing circuits

Cited by 51 publications

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Design of fine-grained sequential approximate circuits using probability-aware fault emulation

Energy-Efficient Digital Processing via Approximate Computing

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