Choose your AI Containers™(s)™ via AnyConnect Console
In edge inference, the classification and/or decision of a neural network typically happens at the edge (on the camera). While inferring on one single device is relatively straightforward, deploying different neural networks from various frameworks, securely, over the air, to millions of different edge devices in the field—is a lot more complicated.
AnyConnect provides a solution to this problem through an AI Store™, enabling users to deploy AI Containers™ securely to the edge, at scale, with access control. The AnyConnect Console monitors all devices on the platform, and provides the ability to centrally view, control and manage deployed containers.
- Select AI Containers™(s)™ from our AI Store™.
- Optionally, upload your own AI Containers™(s)™ to our AI Store™.
- Deploy selected AI Containers™ to your cameras with AnyConnect’s OTA Programming feature.
AnyConnect’s Smarter AI™ Camera Platform offers a repository of AI Containers™ in its cloud. The repository holds containers with both free and paid trained neural networks, as well as some software logic.
AnyConnect’s Smarter AI™ Camera Platform deploys AI Containers™ to the edge, seamlessly, securely, and over-the-air. The system will deliver the container with the right framework, to the right edge device, and its neural network accelerator—automatically. This system supports heterogeneous edge device deployments with different types of edge inference accelerators, like CPUs, GPUs, Intel Movidius, Google Coral Edge TPU.
The AnyConnect console allows you to manage and monitor the deployment of AI Containers™ to the edge seamlessly. The management system built into the platform will automatically convert the trained neural networks and their associated logic to the right frameworks needed by your edge device infrastructure.
Our platform will also provide statistics on the quality of inferences centrally.
AI Containers™ help you to solve plumbing, deployment & security challenges related to the deployment of AI applications to the edge. Although chipmakers have created common AI runtimes, such as Intel OpenVino, Qualcomm Neural Processing SDK, NVIDIA JetPack SDK, etc. to simplify porting AI Models to their chips, many problems remain as deploying an AI Application to the edge on a new product always requires long, complicated and costly engineering efforts. Let’s look at what AI Containers™ look like.
AnyConnect’s AI Containers™ have four main parts – Egresses, Ingresses, Configuration Interface, and the AI Compute, Security & Storage Interface. AI Containers™ enable system administrators to deploy an AI Application to a new product graphically in minutes. Let’s dive into each of those four sections.
All video, audio, and data (e.g., sensor data, positioning, etc.) streams required by an AI Application is explicitly defined in the Ingress part of AI Containers™. The Ingress side specifies the format of streams in detail, such as resolution, framerate, and color for video, resolution & number of channels for audio, etc. As an administrator, you’ll know before deploying an AI Containers™ Over-the-Air (OTA), if this container is compatible with the camera product. For instance, does this product have a suitable AI Accelerator, does it have the required sensors/imagers/microphones, and finally, does it have enough available compute capacity.
AI Models’ inferences format (what AI Models predict/recognize/classify) are standardized and directly usable to create events and notifications. On top of that, the egress interface provides inference quality metrics. Inference quality metrics are recorded in AnyConnect’s cloud to provide an understanding of the AI Model’s inference quality per camera and over time.
As AI Models come from different suppliers and AI Containers™ can host multiple models, the configuration of these models and the associated software logic is complicated. AI Containers™ standardize the configuration of AI Models and the embedded software logic, ensuring easy and engineering department-free deployments.
AI Containers™ do not replace or add layers between AI Models and their runtimes; they offer unified, standardized, and monitored access to compute, storage (slow storage such as SD card and fast storage such as NVME SSD) as well as security resources. Security is critical as many AI models come with copy protection, either encryption or a Digital Right Management (DRM). By providing access to Crypto Cores, such as Trust Zone and Trusted Platform Modules (TPMs), AI Containers™ allow secured AI Models on almost any device.