RISC-V Evolution: From Early Computing to Tomorrow's Tech Promise
7 minutes read
Be the first to know.
Get our edge ai weekly email digest.
Tagged with
Edge AI
ORGANIZATIONS. SHAPING THE INDUSTRY.
Edge Impulse
Machine Learning
AI for any edge device from MCUs, NPUs, CPUs, GPUs, gateways, and beyond.
24 Posts
Latest Posts
Optimizing AI for the Future: Model Efficiency and Scalability
Gesture recognition, leveraging the advanced capabilities of embedded devices and streamlined through specialized platforms is creating new means of human-machine interactions, paving the way for more intuitive and user-friendly device interfaces.
Model efficiency research is enhancing power efficiency and boosting AI performance.
How on-device and cloud AI will foster the advancement and scalability of next-generation generative AI experiences
Qualcomm's Pioneering Approach to Hybrid AI: Merging Cloud Processing with On-Device Efficiency and Scale
Seamless and intuitive user experiences using speech-recognition technology are enabling us to interact with a device simply through voice commands.
Article 6 of Bringing Intelligence to the Edge Series: The utilization of edge AI facilitates advanced system optimization, predictive maintenance, and improved anomaly detection, greatly advancing technological capabilities across varied fields.
This is how Sevensense tests its curring-edge Visual SLAM technology to ensure robust performance.
Edge AI platforms can assist engineers to monitor battery health systems.
Article 4 of Bringing Intelligence to the Edge Series: AI is proving to be a more precise and time-efficient tool in processing the big data crunch by recognizing patterns and noticing inconsistencies in real-time.
Automating the food supply is key to securing future food supply chains.
Join us for the premier edge AI event, with presentations and demonstrations from top technology leaders and innovators.
Article 3 of Bringing Intelligence to the Edge Series: Balancing the critical metrics of accuracy, power consumption, latency, and memory requirements is key to unlocking the potential of Tiny Machine Learning (TinyML) in low-power microcontrollers and edge computing.
Article #4 of Spotlight on Innovations in Edge Computing and Machine Learning: Discover the integration of TinyML and wearable tech as we delve into a project that detects falls in real-time, potentially saving lives in our aging population.
