When you purchase through links in our articles, we may earn a small commission. Laptop docking stations may evolve into'AI docks' with video and apps Synaptics also says it will offer both USB4 chips as well as DisplayLink in the future. For better or worse, laptop docking stations have generally been "dumb" devices. Synaptics and its customers are hoping to change that. Right now, there are two main technologies that "compete" in the docking stations space: USB4 (which Intel puts its own spin on with its Thunderbolt 4 technology) and DisplayLink (a technology Synaptics bought in 2020).

Banana Pi BPI-M6 is the next generation single board computer from Banana Pi in 2022,It is powered by Senary(Synaptics) VS680 quad-core Cortex-A73 (2.1GHz) and One Cortex-M3 processor,Imagination GE9920 GPU.and NPU Up to 6 .75Tops. Onboard 4GB LPDDR4 memory and 16GB EMMC storage, and supports 4 USB 3.0 interface, a gigabit network port.onboard 1 HDMI-rx port and 1 Hdmi-tx port. VideoSmart VS680 solution, an industry-first edge computing SoC that combines a CPU, NPU, and GPU. This new multimodal platform with integrated neural network accelerator is purpose built with perceptive intelligence for applications including smart displays, smart cameras, set-top-boxes and media streamers.The Synaptics VideoSmart VS680 is a multimedia powerhouse that combines a Qdeo 4K-video engine, an audio processor capable of far-field keyword detection and voice recognition, and a proprietary SyNap deep-learning accelerator (DLA). Another new feature is an ISP with HDR capabilities that can handle two 4K cameras. Previous VideoSmart products target the streaming-video set-top-box (STB) market, but the VS680 aims for a broader range of smart-home devices.

Machine learning algorithms have opened up a realm of possibilities to enable vision embedded in products that make our home, workspaces and the places in between safer and more efficient. To truly realize the potential of smart vision in more use cases, developers need more power-efficient, and more flexible embedded solutions that can operate on batteries, be easy to install and maintain, and still deliver the vision performance required to provide effective and intelligent sensing of the things we want to detect and monitor. New advances in ML modeling and processing hold the key to widespread adoption of smart cameras. Affordable remote visual monitoring used to mean an infrared motion detector: inexpensive, autonomous, but not necessarily effective. A friend of mine recently protected his back yard with a set of internet-connected video cameras.

Synaptics Incorporated announced a partnership with Edge Impulse, the leading development platform for machine learning on edge devices. The partnership combines Synaptics' Katana Ultra Low-Power Edge AI Platform with the Edge Impulse software development platform used by thousands of embedded developers to create, train and deploy custom models for a wide range of AI applications. The Edge Impulse Embedded ML Platform allows developers to create production-ready models, faster and efficiently. Additionally, it enables easy testing, training, and model optimization in a complete MLOps environment. For example, developers can create new tinyML models in as little as five minutes using an innovative and interactive cloud-based platform without writing a single line of code.

Synaptic is a powerful cognitive engine that incorporates predictive analytics, machine-learning, control systems and feedback, natural language processing, and other proprietary algorithms to personalize projects, campaigns or pitches at a human and behavioral level. Synaptic is modeled on neuron activity and learning mechanisms occurring in the human brain, exceeding the capabilities of traditional deep learning systems. He interacts with the real world and learns in real-time–reinforcing the intelligence as new concepts and situations arise. This means that Synaptic owns the same limitless understanding of information that humans enjoy. Synaptic allows you to analyze your own unstructured data to unearth meaningful insights hidden within.

Neural networks and machine learning, a field of computer science that heavily relies on them to give computers the ability to learn without being explicitly programmed, seem to be everywhere these days. Scientists want to use advanced neural networks to find energy materials, the Wall Street would like to train neural networks to manage hedge funds and pick stocks, and Google has been relying on neural networks to deliver highly accurate translations and transcriptions. At the same time, powerful, consumer-grade hardware for machine learning is getting more affordable. For example, Nvidia has recently introduced its Titan V graphics card, which is targeted specifically at machine learning developers, who would like to create neural networks without paying for a special server to handle all the complex math operations involved in machine learning. With the future of machine learning looking so bright and the necessary computational resources being so available, many JavaScript developers wonder what's the easiest way how to create neural networks in JavaScript. If you count yourself among them, this article is for you.

Conexant got Synaptics in the far-field voice-enabled market represented by products like the Amazon Echo, Google Home and Apple HomePod. In fact, Synaptics is designed into HomePod, and most Alexa-based smart devices. I can foresee in the not so distant future where we see voice input on just about every kind of device in the many rooms in the home, in the car and even at work, globally. Look at the way Samsung is driving Bixby into every one of Samsung's consumer devices. I also see voice as a big driver in the AR and VR market as voice enhances the UX as the user can't touch anything or of course, use a mouse.

There is definitely an established machine learning ecosystem, or, perhaps more accurately, a small set of established machine learning ecosystems. For research it would seem that the undisputed champion of machine learning ecosystems is centered on Python and its many libraries which support the data preparation and subsequent machine learning process itself, whether it be via scikit-learn, one of the many deep learning libraries available, or home-spun and highly specialized tools for achieving the same goals. This says nothing of the great support tools that grow up around the edges of the ecosystem, some of which become polished and useful enough to carve out their own eventual niche. As those in industry would be the first to let me know, Python is not the only option. There are Java-based tools (Deeplearning4j, Weka), those integrated with Apache Spark and/or Hadoop (MLlib, Mahout), C solutions (TensorFlow is written in C, as are many others in the Python ecosystem), and even those for Clojure, F#, Rust, and a whole host of other languages, environments, and ecosystems.

Currently, I am an IT student in college. This semester, I had a really interesting course which I choose my own topic to study, and create my own project. So I decided to learn and work on something very interesting and unique. While I was exploring various topic, I found a video tutorial about Neural Network, and I was really interested in learning this topic. After watching the tutorial video, I come up with an idea to implement a neural network program that learns how to play Flappy Bird game.

There is definitely an established machine learning ecosystem, or, perhaps more accurately, a small set of established machine learning ecosystems. For research it would seem that the undisputed champion of machine learning ecosystems is centered on Python and its many libraries which support the data preparation and subsequent machine learning process itself, whether it be via scikit-learn, one of the many deep learning libraries available, or home-spun and highly specialized tools for achieving the same goals. This says nothing of the great support tools that grow up around the edges of the ecosystem, some of which become polished and useful enough to carve out their own eventual niche. As those in industry would be the first to let me know, Python is not the only option. There are Java-based tools (Deeplearning4j, Weka), those integrated with Apache Spark and/or Hadoop (MLlib, Mahout), C solutions (TensorFlow is written in C, as are many others in the Python ecosystem), and even those for Clojure, F#, Rust, and a whole host of other languages, environments, and ecosystems.