Mixed Precision ℓ1 Solver for Compressive Depth Reconstruction: An ADMM Case Study

Yun Wu, Andrew Michael Wallace, Andreas Aßmann, Brian Stewart

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Rapid reconstruction of depth images from sparsely sampled data is important for many machine learning applications, including robot or vehicle assistance or autonomy, which require low power LiDAR sensing for eye safety, and resource reduction for FPGA or solid state implementation, especially with constrained energy budgets. A new compressive depth reconstruction design approach is proposed using a compact ADMM solver for the lasso problem, which varies the precision scaling in an iterative optimization process. Implementations on an FPGA architecture show over 55% savings in hardware resources and 78% in power with only minor reduction in reconstructed depth image quality compared to single float precision.

Original languageEnglish
Title of host publication2021 IEEE Workshop on Signal Processing Systems (SiPS)
Number of pages6
ISBN (Electronic)9781665401449
Publication statusPublished - 13 Nov 2021
Event2021 International Workshop on Signal Processing Systems - Coimbra, Portugal
Duration: 19 Oct 202121 Oct 2021


Workshop2021 International Workshop on Signal Processing Systems
Abbreviated titleSiPS 2021
Internet address


  • Alternating Direction Method of Multipliers
  • Compressive Sensing
  • Depth Reconstruction
  • Field-Programmable Gate Array
  • Mixed Precision

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture


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