SALSA: Systematic logic synthesis of approximate circuits

By Swagath Venkataramani, Amit Sabne, V. Kozhikkottu, Kaushik Roy, Anand Raghunathan. DAC Design Automation Conference 2012, 2012

356 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ad07a9594e920f1af2bd222fe293e98a3afbf814

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In Electrical and Electronic Engineering

Citations in the corpus

Efficient embedded learning for IoT devices (2016, methodology): “To address these challenges, several approximation techniques at various layers of the computing stack, including software [17]–[20], architecture [12], [21]–[24] and circuits [25]–[29], have been proposed.”
Approximate Computing: A Survey (2016, background): “SALSA [56] encodes quality constraints into a virtual logic function and simplifies the circuit with Bapproximation don't cares[ that arise from quality relaxation.”
EvoApproxSb: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods (2017, background): “In the case of general-purpose methods (e.g. SALSA [10] and SASIMI [11]), adders and multipliers serve as one of many circuit classes that can be approximated.”
AxNN: Energy-efficient neuromorphic systems using approximate computing (2014, background): “Approximate computing [8–14] is an emerging design paradigm that leverages the intrinsic resilience of applications, i.e. , their ability to produce results of acceptable quality even when many of their computations are performed in an approximate manner.”
Approximate computing: An emerging paradigm for energy-efficient design (2013, background): “Automated synthesis of approximate circuits is recently discussed in [41] for large and complex circuits under error constraints.”
Energy-efficient ConvNets through approximate computing (2016, methodology): “Static examples in literature make use of approximate digital building blocks for arithmetic functions [11], or use approximate logic synthesis methods [20].”
Analysis and characterization of inherent application resilience for approximate computing (2013, methodology): “For example, the bit error profile of an approximate adder proposed in [16] is presented in Figure 4.”
DRUM: A Dynamic Range Unbiased Multiplier for approximate applications (2015, methodology): “While most of the focus has been on the design of approximate arithmetic units, some recent efforts have been devoted to developing approximate logic synthesis [2], [18], [19], where techniques are proposed for automated synthesis of circuits into approximate hardware design.”
A Survey of Techniques for Approximate Computing (2016, background): “…algorithms [Samadi et al. 2013; Zhang et al. 2015; Sidiroglou et al. 2011; McAfee and Olukotun 2015; Rin-genburg et al. 2014], divide and conquer [Venkataramani et al. 2012], gradient descent search [Ranjan et al. 2015], genetic algorithms [Ansel et al. 2011] and integer linear programming…”