Brightening the Optical Flow through Posit Arithmetic

被引:4
作者
Saxena, Vinay [1 ]
Reddy, Ankitha [1 ]
Neudorfer, Jonathan [1 ]
Gustafson, John [3 ]
Nambiar, Sangeeth [1 ]
Teupers, Rainer [2 ]
Merchant, Farhad [2 ]
机构
[1] Bosch Res & Technol Ctr India, Bangalore, Karnataka, India
[2] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, Aachen, Germany
[3] Natl Univ Singapore, Singapore, Singapore
来源
PROCEEDINGS OF THE 2021 TWENTY SECOND INTERNATIONAL SYMPOSIUM ON QUALITY ELECTRONIC DESIGN (ISQED 2021) | 2021年
关键词
Optical flow; computer arithmetic; posits; floating-point; Lucas-Kanade algorithm;
D O I
10.1109/ISQED51717.2021.9424360
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
As new technologies are invented, their commercial viability needs to be carefully examined along with their technical merits and demerits. The posit (TM) data format, proposed as a drop-in replacement for IEEE 754 (TM) float format, is one such invention that requires extensive theoretical and experimental study to identify products that can benefit from the advantages of posits for specific market segments. In this paper, we present an extensive empirical study of posit-based arithmetic vis-a-vis IEEE 754 compliant arithmetic for the optical flow estimation method called Lucas-Kanade (LuKa). First, we use SoftPosit and SoftFloat format emulators to perform an empirical error analysis of the LuKa method. Our study shows that the average error in LuKa with SoftPosit is an order of magnitude lower than LuKa with SoftFloat. We then present the integration of the hardware implementation of a posit adder and multiplier in a RISC-V open-source platform. We make several recommendations, along with the analysis of LuKa in the RISC-V context, for future generation platforms incorporating posit arithmetic units.
引用
收藏
页码:463 / 468
页数:6
相关论文
共 50 条
  • [31] TraSFlow: learning traditional optical flow proposal and segmentation for optical flow estimation improvement
    Ammar, Anis
    Ghozzi, Rim
    Souani, Chokri
    SIGNAL IMAGE AND VIDEO PROCESSING, 2025, 19 (06)
  • [32] ON THE NONLINEAR ANALYSIS OF OPTICAL FLOW
    Xia, Shengxiang
    Yin, Yanmin
    TOPOLOGICAL METHODS IN NONLINEAR ANALYSIS, 2016, 48 (02) : 661 - 676
  • [33] Optical Flow on Moving Manifolds
    Bauer, Martin
    Grasmair, Markus
    Kirisits, Clemens
    SIAM JOURNAL ON IMAGING SCIENCES, 2015, 8 (01): : 484 - 512
  • [34] A ROBUST OPTICAL FLOW COMPUTATION
    Lu Zongqing Xie Weixin Pei Jihong (School of Electronic Engineering
    Journal of Electronics(China), 2007, (05) : 635 - 641
  • [35] Exploiting Discontinuities in Optical Flow
    William B. Thompson
    International Journal of Computer Vision, 1998, 30 : 163 - 173
  • [36] Sparsity in optical flow and trajectories
    Joel Gibson
    Oge Marques
    Signal, Image and Video Processing, 2016, 10 : 487 - 494
  • [37] A torus model for optical flow
    Adams, Henry
    Bush, Johnathan
    Carr, Brittany
    Kassab, Lara
    Mirth, Joshua
    PATTERN RECOGNITION LETTERS, 2020, 129 : 304 - 310
  • [38] Robust Optical Flow Integration
    Crivelli, Tomas
    Fradet, Matthieu
    Conze, Pierre-Henri
    Robert, Philippe
    Perez, Patrick
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2015, 24 (01) : 484 - 498
  • [39] Exploiting discontinuities in optical flow
    Thompson, WB
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 1998, 30 (03) : 163 - 173
  • [40] Parallel computation of optical flow
    Dopico, AG
    Correia, MV
    Santos, JA
    Nunes, LM
    IMAGE ANALYSIS AND RECOGNITION, PT 2, PROCEEDINGS, 2004, 3212 : 397 - 404