Constrained Precision Tuning

Khalifa, Dorra Ben; Martel, Matthieu

doi:10.1109/codit55151.2022.9804011

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Once the semantic equations are generated, POPiX calls a linear solver to find the minimal number of significant bits needed for the input and intermediate variables of the program. To obtain the optimal solution to our system of constraints, cost functions are given to the linear solver as optimization objective functions [19]. Depending on which cost function is used, different criteria may be considered for the tuning.…”

Section: ) Precision Tuning Frameworkmentioning

confidence: 99%

On the Functional Properties of Automatically Generated Fixed-Point Controllers

Khalifa,

Martel

2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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The implementation of control algorithms typically starts with the development of executable models and prototype implementations, e.g. in C, running on desktop computers before being ported to the target embedded architecture. Often, this latter architecture uses fixed-point arithmetic that differs in terms of accuracy from the floating-point arithmetic used by the desktop computer. In this article, we show that our POPiX tool is capable of automatically transforming floatingpoint codes into fixed-point ones while preserving the functional properties of the original control algorithms and optimizing resources in terms of memory and power consumption. We experiment POPiX on two widely used algorithms: a PID controller and a Kalman filter. Our experimental results validate, at the functional level, the code generation performed automatically by POPiX.

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Section: ) Precision Tuning Frameworkmentioning

confidence: 99%

On the Functional Properties of Automatically Generated Fixed-Point Controllers

Khalifa,

Martel

2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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Implementation and Synthesis of Math Library Functions

Briggs,

Lad,

Panchekha

2024

Proc. ACM Program. Lang.

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Achieving speed and accuracy for math library functions like exp, sin, and log is difficult. This is because low-level implementation languages like C do not help math library developers catch mathematical errors, build implementations incrementally, or separate high-level and low-level decision making. This ultimately puts development of such functions out of reach for all but the most experienced experts. To address this, we introduce MegaLibm, a domain-specific language for implementing, testing, and tuning math library implementations. MegaLibm is safe, modular, and tunable. Implementations in MegaLibm can automatically detect mathematical mistakes like sign flips via semantic wellformedness checks, and components like range reductions can be implemented in a modular, composable way, simplifying implementations. Once the high-level algorithm is done, tuning parameters like working precisions and evaluation schemes can be adjusted through orthogonal tuning parameters to achieve the desired speed and accuracy. MegaLibm also enables math library developers to work interactively, compiling, testing, and tuning their implementations and invoking tools like Sollya and type-directed synthesis to complete components and synthesize entire implementations. MegaLibm can express 8 state-of-the-art math library implementations with comparable speed and accuracy to the original C code, and can synthesize 5 variations and 3 from-scratch implementations with minimal guidance.

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Constrained Precision Tuning

Cited by 2 publications

References 23 publications

On the Functional Properties of Automatically Generated Fixed-Point Controllers

On the Functional Properties of Automatically Generated Fixed-Point Controllers

Implementation and Synthesis of Math Library Functions

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