FabRobotics: Fusing 3D Printing with Mobile Robots to Advance Fabrication, Robotics, and Interaction
Name
3623509.3633365.pdf
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70.05 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Bhattacharya, Ramarko
Lindstrom, Jonathan
Taka, Ahmad
Nisser, Martin
Mueller, Stefanie
Nakagaki, Ken
Date Issued
February 11, 2024
Publisher
ACM|Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction
Citation
Bhattacharya, Ramarko, Lindstrom, Jonathan, Taka, Ahmad, Nisser, Martin, Mueller, Stefanie et al. 2024. "FabRobotics: Fusing 3D Printing with Mobile Robots to Advance Fabrication, Robotics, and Interaction."
Version
Final published version
Abstract
Modern vector processors support a wide variety of instructions for fixed-point digital signal processing. These instructions support a proliferation of rounding, saturating, and type conversion modes, and are often fused combinations of more primitive operations. While these are common idioms in fixed-point signal processing, it is difficult to use these operations in portable code. It is challenging for programmers to write down portable integer arithmetic in a C-like language that corresponds exactly to one of these instructions, and even more challenging for compilers to recognize when these instructions can be used. Our system, Pitchfork, defines a portable fixed-point intermediate representation, FPIR, that captures common idioms in fixed-point code. FPIR can be used directly by programmers experienced with fixed-point, or Pitchfork can automatically lift from integer operations into FPIR using a term-rewriting system (TRS) composed of verified manual and automatically-synthesized rules. Pitchfork then lowers from FPIR into target-specific fixed-point instructions using a set of target-specific TRSs. We show that this approach improves runtime performance of portably-written fixed-point signal processing code in Halide, across a range of benchmarks, by geomean 1.31x on x86 with AVX2, 1.82x on ARM Neon, and 2.44x on Hexagon HVX compared to a standard LLVM-based compiler flow, while maintaining or improving existing compile times.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3623509.3633365
