#real-time-inference News & Analysis

6 articles tagged with #real-time-inference. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · 5d ago7/10

🧠

CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models

Researchers introduce CaMBRAIN, a causal state space model based on Mamba architecture that enables real-time, continuous EEG signal processing with linear-time complexity. The model achieves state-of-the-art results across multiple datasets while processing signals >10x faster than existing attention-based methods, overcoming critical limitations in handling variable-length brain activity recordings.

AIBullisharXiv – CS AI · Mar 56/10

🧠

LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics

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.

AIBullisharXiv – CS AI · Mar 56/10

🧠

Chimera: Neuro-Symbolic Attention Primitives for Trustworthy Dataplane Intelligence

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.

AINeutralarXiv – CS AI · May 116/10

🧠

Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey

A comprehensive academic survey examines edge deep learning—the integration of deep learning with edge computing—and its applications in computer vision and medical diagnostics. The paper categorizes hardware platforms, reviews model optimization techniques like compression and lightweight design, and identifies future challenges for deploying neural networks on resource-constrained devices.

AIBullisharXiv – CS AI · Mar 37/106

🧠

TinyVLM: Zero-Shot Object Detection on Microcontrollers via Vision-Language Distillation with Matryoshka Embeddings

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.

AINeutralarXiv – CS AI · Apr 145/10

🧠

Real-Time Voicemail Detection in Telephony Audio Using Temporal Speech Activity Features

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.