LENS: Edge Scene Memory Acceleration System on AMD KR260 FPGA
Overview
This research presents LENS, an edge scene-memory acceleration system for resource-constrained robots and autonomous vehicles. LENS runs on an AMD KR260 FPGA/ARM platform, combining an INT16 SCR accelerator and a fixed-point 3DGS renderer to enable efficient localization and rendering without a GPU. The system achieves performance comparable to GPU-based methods while reducing power consumption by up to 5.7x.
From arXiv Robotics — research abstracts
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · localization accuracy: 25.42 other · Basis not reported
“rites alpha transmittance as a log-sum-exp recurrence and removes provably useless streamed Gaussians. All LENS latency, power, and quality results are board-measured. The SCR engine preserves DSAC* localization accuracy at 25.42 ms, the on-board renderer reaches 34.11 dB on Cambridge against ACEsplat's 33.20, novel-view synthesis on RealEstate10K reaches 27.84 dB against 26.11, and all ten Wayspots scenes reach the”
Exact source · revision 2Source owner not reported
Reported quantity · rendering quality: 34.11 other · Basis not reported · Differing source assertions
“rites alpha transmittance as a log-sum-exp recurrence and removes provably useless streamed Gaussians. All LENS latency, power, and quality results are board-measured. The SCR engine preserves DSAC* localization accuracy at 25.42 ms, the on-board renderer reaches 34.11 dB on Cambridge against ACEsplat's 33.20, novel-view synthesis on RealEstate10K reaches 27.84 dB against 26.11, and all ten Wayspots scenes reach the”
Exact source · revision 2Source owner not reported
Reported quantity · novel-view synthesis quality: 27.84 other · Basis not reported
“rites alpha transmittance as a log-sum-exp recurrence and removes provably useless streamed Gaussians. All LENS latency, power, and quality results are board-measured. The SCR engine preserves DSAC* localization accuracy at 25.42 ms, the on-board renderer reaches 34.11 dB on Cambridge against ACEsplat's 33.20, novel-view synthesis on RealEstate10K reaches 27.84 dB against 26.11, and all ten Wayspots scenes reach the”
Exact source · revision 2Source owner not reported
Reported quantity · rendering quality: 26.11 other · Basis not reported · Differing source assertions
“rites alpha transmittance as a log-sum-exp recurrence and removes provably useless streamed Gaussians. All LENS latency, power, and quality results are board-measured. The SCR engine preserves DSAC* localization accuracy at 25.42 ms, the on-board renderer reaches 34.11 dB on Cambridge against ACEsplat's 33.20, novel-view synthesis on RealEstate10K reaches 27.84 dB against 26.11, and all ten Wayspots scenes reach the”
Exact source · revision 2Source owner not reported
Reported quantity · rendering quality: 27.84 other · Basis not reported · Differing source assertions
“rites alpha transmittance as a log-sum-exp recurrence and removes provably useless streamed Gaussians. All LENS latency, power, and quality results are board-measured. The SCR engine preserves DSAC* localization accuracy at 25.42 ms, the on-board renderer reaches 34.11 dB on Cambridge against ACEsplat's 33.20, novel-view synthesis on RealEstate10K reaches 27.84 dB against 26.11, and all ten Wayspots scenes reach the”
Exact source · revision 2Source owner not reported
Evidence dependency changes (3)
- 2026-10-30T22:19:24.840Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:19:18.144Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-26T20:40:47.166Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalLocalization and Neural Splatting on the Edge: An FPGA Scene Memory for Robots
This research presents LENS, an edge scene-memory acceleration system for resource-constrained robots and autonomous vehicles. LENS runs on an AMD KR260 FPGA/ARM platform, combining an INT16 SCR accelerator and a fixed-point 3DGS renderer to enable efficient localization and rendering without a GPU. The system achieves performance comparable to GPU-based methods while reducing power consumption by up to 5.7x.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).