Artificial Intelligence for Edge Devices
In many cases, AI processing needs to happen locally (with or without access to network/Internet), rather than in the cloud. Such approach is desirable due to many aspects like: latency requirements, security or privacy concerns, and communication bandwidth limitations or even lack of it. It applies to various verticals like: mobile, (I)IoT, automotive, AR, robotics, and drones. On the other hand, machine and deep learning algorithms tend to be computationally very complex. This makes the processing on constrained devices difficult. The computing power and available energy are very much limited in such environments.
At byteLAKE, we help our clients bring Artificial Intelligence to the Edge and eventually enable machines to learn and become smart.
We enable AI at the edge through:
- Algorithms optimization and fine tuning to take maximum advantage of given processing units, accelerators and overall hardware architecture.
- Optimum usage of precious resources by selecting appropriate data types.
- Energy-efficient techniques implementation and development to reduce the energy consumption for real-time data processing.
- Pre-trained models development and deployment.