CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910249941729280 |
|---|---|
| author | Min, Kyeongpil Choi, Jongin Lee, Kyeongwon Lee, Woojoo |
| author_facet | Min, Kyeongpil Choi, Jongin Lee, Kyeongwon Lee, Woojoo |
| contents | Contrast maximization (CMAX) is a direct geometric framework for event-based motion estimation, but its iterative warp-and-accumulate pipeline incurs input-dependent computation and frequent memory accesses, challenging real-time, low-power edge deployment. We present CMAX-CAMEL, a coarse-to-fine adaptive, memory-efficient, low-power edge processor for CMAX. CMAX-CAMEL combines a runtime-adaptive execution strategy with a memory-centric processor architecture. It adjusts coarse-to-fine execution according to the observed event distribution, prioritizing stages likely to improve estimation accuracy while suppressing low-value iterations and unnecessary stage transitions. Architecturally, a banked parallel memory organization sustains real-time throughput while reducing latency, and a subsampling-coupled accumulation structure lowers memory-access activity along the warp-and-accumulate dataflow. On a Virtex FPGA prototype operating at 200 MHz, CMAX-CAMEL improves estimation accuracy by up to 19% over fixed coarse-to-fine schedules, reduces processing latency by 53.3%, lowers effective memory accesses by 42%, and cuts total system energy by 52.2%, including adaptation overheads. These results show that CMAX-CAMEL is an HW-SW co-design that co-optimizes execution policy and data movement for real-time, low-power event-based motion estimation at the edge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24017 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization Min, Kyeongpil Choi, Jongin Lee, Kyeongwon Lee, Woojoo Hardware Architecture Contrast maximization (CMAX) is a direct geometric framework for event-based motion estimation, but its iterative warp-and-accumulate pipeline incurs input-dependent computation and frequent memory accesses, challenging real-time, low-power edge deployment. We present CMAX-CAMEL, a coarse-to-fine adaptive, memory-efficient, low-power edge processor for CMAX. CMAX-CAMEL combines a runtime-adaptive execution strategy with a memory-centric processor architecture. It adjusts coarse-to-fine execution according to the observed event distribution, prioritizing stages likely to improve estimation accuracy while suppressing low-value iterations and unnecessary stage transitions. Architecturally, a banked parallel memory organization sustains real-time throughput while reducing latency, and a subsampling-coupled accumulation structure lowers memory-access activity along the warp-and-accumulate dataflow. On a Virtex FPGA prototype operating at 200 MHz, CMAX-CAMEL improves estimation accuracy by up to 19% over fixed coarse-to-fine schedules, reduces processing latency by 53.3%, lowers effective memory accesses by 42%, and cuts total system energy by 52.2%, including adaptation overheads. These results show that CMAX-CAMEL is an HW-SW co-design that co-optimizes execution policy and data movement for real-time, low-power event-based motion estimation at the edge. |
| title | CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2605.24017 |