Edge Computing

Running models locally on-device can improve response speed and data security, avoiding the latency and privacy issues associated with cloud data uploads.

Edge computing deploys computational power close to where data is generated, offering two key advantages: reduced latency and less data transmission to the cloud.

Command execution latency on a local area network can be as low as under 5 milliseconds, far superior to the 50–300 millisecond round-trip latency of cloud-based solutions.

Running AI models locally requires trade-offs among privacy, cost, and performance, and local model capabilities may not match those of top-tier cloud-based large models.

Edge AI devices adopt a typical "thin terminal + thick backend" architecture, where the terminal handles data collection and result display while large model inference and tool calls are completed at the gateway or in the cloud.

Gartner predicts that by 2025 more than 75% of enterprise-generated data will be created and processed outside traditional data centers or the cloud.

Edge computing architectures reduce latency from hundreds of milliseconds to the millisecond level, but edge devices have limited compute power, so models must be compressed and quantized to be deployed, and recognition accuracy may be lower than that of large cloud models.

The trend of AI spreading from centralized cloud services to the edge and endpoints is clear.

Physical systems are extremely sensitive to latency, and inference must be able to run efficiently on edge devices, imposing requirements on model compression and hardware co-design that differ from those of cloud AI.

Edge computing can first perform feature extraction on high-frequency signals such as vibration and sound, uploading only compressed summaries, thereby reducing transmission pressure and lowering the risk of raw data exposure

The Industry 4.0 framework collects data via sensors, performs preliminary processing through edge computing, and uses cloud-based machine learning models to identify equipment degradation patterns and predict failures, enabling the transition from planned maintenance to predictive maintenance.

Local inference on edge devices avoids data uploads and is inherently privacy-friendly.

Edge computing deploys computing power at the edge where data is generated, enabling inference to be completed within milliseconds, whereas cloud computing links have network latency of tens to hundreds of milliseconds.

SiMa.ai focuses on edge computing processors (physical AI chips) for real physical world application scenarios.

Pushing model inference capabilities down to edge nodes can significantly reduce response latency for end users.

The memory and computing power available on edge devices are often measured in MB and milliwatts, differing from data center GPU clusters by several orders of magnitude.

Industrial edge endpoints typically run lightweight real-time inference models that have been distilled and quantized, as well as local rule engines, while full large language models, due to their massive parameter counts, are usually run on the cloud or private compute platforms and are only used for offline analysis and decision support.

The control logic of industrial Agents must be pushed down to the edge; the cloud only handles data analysis, model training, and cross-site coordination, and on-site execution control cannot rely on the cloud.

The first principle of industrial Agent architecture design is to place real-time control on the edge side rather than in the cloud, with the cloud handling only analysis, training, and coordination tasks.

Edge computing can reduce network latency from 200ms across continents to within 10ms within the same city