Noise-Aware Dynamical System Compilation for Analog Devices with Legno
Name
asplos20-legno.pdf
Description
Accepted version
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1.91 MB
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Adobe PDF
Checksum (MD5)
c487611da6e829a10f7f69f9cb4d2971
Author(s)
Achour, Sara
Rinard, Martin C
Date Issued
March 2020
Journal
International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS
Publisher
Association for Computing Machinery (ACM)
Citation
Achour, Sara and Martin Rinard. “Noise-Aware Dynamical System Compilation for Analog Devices with Legno.” Pape in the Proceedings of the Twenty-Fifth International Conference on Architectural Supportfor Programming Languages and Operating Systems (ASPLOS ’20), Lausanne, Switzerland, March 16–20, 2020, Association for Computing Machinery (ACM) © 2020 The Author(s)
Version
Author's final manuscript
Abstract
Reconfigurable analog devices are a powerful new computing substrate especially appropriate for executing computationally intensive dynamical system computations in an energy efficient manner. We present Legno, a compilation toolchain for programmable analog devices. Legno targets the HCDCv2, a programmable analog device designed to execute general nonlinear dynamical systems. To the best of our knowledge, Legno is the first compiler to successfully target a physical (as opposed to simulated) programmable analog device for dynamical systems and this paper is the first to present experimental results for any compiled computation executing on any physical programmable analog device of this class. The Legno compiler synthesizes analog circuits from parametric and specialized blocks and accounts for analog noise, quantization error, and manufacturing variations within the device. We evaluate the compiled configurations on the Sendyne S100Asy RevU development board on twelve benchmarks from physics, controls, and biology. Our results show that Legno produces accurate computations on the analog device. The computations execute in 0.50-5.92 ms and consume 0.28-5.67 µJ of energy.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/3373376.3378449
