Efficient Reconfigurable Mixed Precision ℓ1 Solver for Compressive Depth Reconstruction

Yun Wu, Andrew Michael Wallace, João F. C. Mota, Andreas Aßmann, Brian Stewart

Research output: Contribution to journalArticlepeer-review

4 Downloads (Pure)


Rapid reconstruction of depth images from sparsely sampled data is important for many applications in machine perception, including robot or vehicle assistance or autonomy. Approximate computing techniques have been widely adopted to reduce resource consumption and increase efficiency in energy and resource constrained systems, especially targeted at FPGA and solid state implementation. Whereas previous work has focused on approximate, but static, representation of data in LiDAR systems, in this paper we show how the flexibility of an arbitrary precision accelerator with fine-grain tuning allows a better trade-off between algorithmic performance and implementation efficiency. A mixed precision framework of ℓ1 solvers is presented, with compact ADMM and PGD, for the lasso problem, enabling compressive depth reconstruction by varying the precision scaling in single bit granularity during the iterative optimization process. Implementing mixed precision ℓ1 solvers on an FPGA with a pipelined architecture for depth image reconstruction across various sensing scenarios, over 74% savings in hardware resources and 60% in power are achieved with only minor reductions in reconstructed depth image quality when compared to single float precision, while over 10% saving in hardware resources and power is achieved compared to relative consistently reduced precision solutions.
Original languageEnglish
Pages (from-to)1083-1099
Number of pages17
JournalJournal of Signal Processing Systems
Issue number10
Early online date23 May 2022
Publication statusPublished - Oct 2022


  • Alternating direction method of multipliers
  • Compressive sensing
  • Depth reconstruction
  • Field-programmable gate array
  • Mixed precision
  • Proximal gradient descent

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Hardware and Architecture


Dive into the research topics of 'Efficient Reconfigurable Mixed Precision ℓ1 Solver for Compressive Depth Reconstruction'. Together they form a unique fingerprint.

Cite this