Use Case: Edge Devices

Deploy Your AI Technology on Any Device

DeepCube’s technology allows the deployment of state-of-the-art deep learning models on edge devices, with minimal processing, memory, and battery requirements. This is critical for any edge device that requires quick responses (low latency), high bandwidth (e.g. video), low battery consumption, data security and privacy (cannot send user data to the cloud), and most importantly, autonomous intelligence without a constant cloud connection.

DeepCube - On Any Device
DeepCube - On Any Device

Deploy Your AI on Any Device

DeepCube’s technology allows the deployment of state-of-the-art deep learning models on edge devices, with minimal processing, memory, and battery requirements. This is critical for any edge device that requires fast response (low latency), high bandwidth (e.g. video), low battery consumption, data security and privacy (cannot send user data to cloud), and most importantly autonomous intelligence without continuous connection to the cloud.

DeepCube - Surveillance Cameras

Surveillance Cameras

Intelligent surveillance cameras have become critical in monitoring and securing locations remotely. For decision making, the data has to be sent to the cloud. Tasks such as continuous monitoring of videos and crowd analysis for example pose two major problems: real-time detection and privacy. DeepCube provides the only technology that allows efficient deployment of deep learning models on surveillance cameras, enabling them to make truly autonomous decision in real-time without any connection to the cloud.

DeepCube - Surveillance Cameras

Surveillance Cameras

Intelligent surveillance cameras have become critical in monitoring and securing locations remotely, and for handling newer tasks such as crowd analysis. However, in order for these devices to make decisions today, they have to send data to the cloud, which creates major issues when it comes to real time detection and privacy. DeepCube provides the only technology that allows for the efficient deployment of deep learning models on surveillance cameras, enabling them to make truly autonomous decision in real-time without a cloud connection.

Drones

Autonomous drones are crucial for tasks such as surveillance and reconnaissance. Today, they utilize deep learning models that run on a remote cloud, which makes real-time detection and decision-making impossible. Additionally, they are struggling to combat the lack of high-quality internet connections. With DeepCube, a deep learning model is deployed on the drone itself, enabling it to make truly autonomous decisions in real-time without constant connectivity.

DeepCube - Drones
DeepCube - Drones

Drones

Autonomous drones are crucial for tasks such as surveillance and reconnaissance. Today, they use deep learning models that are running on a remote cloud, which make the detection and hence decision phase impossible in real-time. Moreover, one of the key current challenges is the lack of high-quality Internet connections. With DeepCube, a deep learning model is deployed on the drone itself, enabling it to make truly autonomous decision in real-time without any need for connectivity.

DeepCube - Autonomous Cars

Autonomous Cars

Deep learning is key for improving the performance and safety of self-driving cars, which currently still require a human to be present and take control if necessary. In a real-world driving situation, predictions and decision making require a certain level of control that cloud connectivity cannot enable. With DeepCube, a deep learning model is deployed in the car itself, enabling it to make truly autonomous decisions in real-time without the need for cloud connectivity.

DeepCube - Autonomous Cars

Autonomous Cars

Deep learning is key for improving performance and safety of self-driving cars, which still need humans to keep an eye and take control when needed. In the real-world driving situation, prediction and decision making require a real-time control which a connectivity to the cloud does not enable. With DeepCube, a deep learning model is deployed in the car itself, enabling it to make truly autonomous decision in real-time without any need for connectivity.

Agricultural Machines

Today, smart farming techniques involve spray machines that can distribute crop disease treatment and pesticides with pinpoint accuracy. These machines are equipped with computer vision and decisions for automated sprays must be made on the spot. Cloud connectivity does not enable speedy real-time decision making and moreover, many of these machines do not have stable internet connectivity. With DeepCube, a deep learning model is deployed in the agricultural robot itself, enabling it to make truly autonomous decisions in real-time without any need for connectivity.

DeepCube - Agricultural Machines
DeepCube - Agricultural Machines

Agricultural Machines

Today, smart farming use crop disease treatments and pesticides with pinpoint accuracy via spray machines. These come equipped with computer vision and automated spray and decision has to be made on the spot. Connection to the cloud does not enable real-time decision and moreover, there can be many situations of poor connectivity quality. With DeepCube, a deep learning model is deployed in the agricultural robot itself, enabling it to make truly autonomous decision in real-time without any need for connectivity.

Transform Your Business

With DeepCube, deploy your AI Technology on any device