Neural‑Accelerated Vision for Real‑Time, Low‑Power Image Processing (Neural Network Acceleration of Image Processing, Tech ID: 23‑019)
Technology Overview: This NJIT technology accelerates image‑processing workloads using neural network–based hardware/algorithm co‑design, enabling real‑time performance at the edge. By mapping common vision operators (e.g., filtering, feature extraction, segmentation) to learned neural primitives and accelerator‑friendly pipelines, the system reduces latency and power draw while preserving accuracy. It is ideal for embedded, battery‑powered, or thermally constrained devices.
Industry Pain Point: Traditional CPU/GPU pipelines are compute‑intensive and power‑hungry for real‑time vision.
NJIT Solution: Neural acceleration delivers low‑latency, energy‑efficient processing suitable for edge AI deployment.
Key Features & Advantages
- Learned neural primitives replace heavy classical operators
- Significant latency and energy reduction
- Supports real‑time, on‑device inference
- Scalable from wearables to autonomous platforms
Development Stage: TRL 3–4 – Architecture‑level and lab validation completed.
Target Markets
- Edge AI & computer vision
- Autonomous/robotic systems
- Smart cameras & surveillance
Market Opportunity
- Computer vision market (2026): ~$20B
- CAGR: ~18–20%
- 2035 projection: ~$90–100B
Commercial & IP Details
Inventors: Shaahin Angizi, Arman Roohi