4 articles tagged with #real-time-inference. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed LiteVLA-Edge, a deployment-oriented Vision-Language-Action model pipeline that enables fully on-device inference on embedded robotics hardware like Jetson Orin. The system achieves 150.5ms latency (6.6Hz) through FP32 fine-tuning combined with 4-bit quantization and GPU-accelerated inference, operating entirely offline within a ROS 2 framework.
Chimera introduces a framework that enables neural network inference directly on programmable network switches by combining attention mechanisms with symbolic constraints. The system achieves line-rate, low-latency traffic analysis while maintaining predictable behavior within hardware limitations of commodity programmable switches.
Researchers developed TinyVLM, the first framework enabling zero-shot object detection on microcontrollers with less than 1MB memory. The system achieves real-time inference at 26 FPS on STM32H7 and over 1,000 FPS on MAX78000, making AI vision capabilities practical for resource-constrained edge devices.
Researchers developed a lightweight machine learning system that detects voicemail greetings versus live human answers in real-time telephony audio with 96.1% accuracy using only temporal speech activity patterns. The system processes calls in 46ms on standard CPUs and has been validated across 77,000 production calls, achieving practical false positive and negative rates suitable for AI calling applications.
