Stochastic Modeling to Accelerate Approximate Operators Simulation

Abstract : —Approximate operators have been developed to overcome the performance limitations of the original accurate arithmetic operators. They trade off the output quality of the operator and its energy consumption, area or delay. To benefit from this trade-off, the logic structure of the original accurate operator is modified. When integrating approximate operators in a complex system, numerous simulations of the application are required to ensure the fulfillment of the application requirements, despite the induced approximations. Because the hardware implementation of approximate operators is not always available in early phases of application prototyping, long software simulation of their complex bit-level structure has to be used. This paper proposes a fast simulator for approximate operators built from the output values of the original accurate operator. The error due to the approximation is modeled by a stochastic process whose features are learned from the errors of the approximate operator. The proposed simulator is compared to the bit-accurate logic-level simulation and to a simulator of approximate operators built on the input values of the operator. Experiments on 10-bit operators show that the proposed method is up to 63× faster than a bit-accurate logic level simulation.
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Justine Bonnot, Karol Desnos, Daniel Menard. Stochastic Modeling to Accelerate Approximate Operators Simulation. ISCAS 2018, May 2018, Florence, Italy. ⟨10.1109/iscas.2018.8350940⟩. ⟨hal-01812706⟩

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