We provide full and partial embedded/edge product development (hardware, firmware, industrial, mechanical, software for Linux and mobile apps) with deep technical management. Additionally, we provide experience to address the various considerations for AI success at the Edge:
- Model Selection and Optimization: AI models that are lightweight and optimized for Edge devices are chosen.
- Resource Constraints: Given that Edge devices typically have limited computational power, memory, and energy, AI model designs need to run efficiently within these constraints and consider trade-offs between model complexity and accuracy.
- Latency and Real-time Inference: Edge devices often require real-time or near-real-time inference. This is achieved through a combination of techniques like model quantization as well as hardware accelerators/design to help achieve faster inference.
- Data Privacy and Security: Given that Edge devices might handle sensitive data, encryption, secure boot mechanisms, and secure communication protocols should be used to implement robust security measures to protect data and prevent unauthorized access.
- Connectivity Issues: Edge devices may experience intermittent or limited connectivity, so it’s important to design AI systems to handle such situations gracefully, with the ability to work offline or in a partially connected state.
- Edge-cloud Collaboration: In some cases, offloading complex AI computations to the cloud might be necessary. Establishing an architecture for a seamless and secure collaboration between Edge devices and the cloud to optimize performance and leverage cloud resources can be very important.
- Edge Device Management: Robust device management is implemented, and solutions monitoring to track the health and performance of Edge devices. This includes remote updates, version control, and error reporting.
- Edge Data Preprocessing: Preprocessing data on an Edge device to reduce data transmission, network costs, and processing overhead can involve data filtering, aggregation, and compression.
• Fault Tolerance and Redundancy: It is important to design the system with fault tolerance in mind as Edge devices might be subject to environmental factors that could lead to failures. - Edge AI Ecosystem: Expertise and experience with the broader ecosystem in which the edge AI system will operate, including compatibility with existing software, communication protocols, and integration with other edge devices or systems, are also needed.
- Regulatory Compliance: Experience with regulatory requirements and certification processes related to radio communications, data processing, and storage in the specific regions where the Edge devices will be deployed is required as well.
- Edge Device Selection: It is important to choose Edge devices that are suitable for the specific AI workload and application.