The Paradigm Shift to the Edge
For years, AI was synonymous with the Cloud. Massive datasets were sent to remote data centers for processing, which then returned a result to the user. While powerful, this model introduces significant friction for modern IoT applications. Edge AI refers to the practice of running machine learning algorithms directly on local hardware.
Cloud AI vs. Edge AI
Traditional AI relies on high-power GPUs and constant connectivity. In contrast, eFabric™ creates models specifically tuned for "always-on" battery-powered devices rather than general cloud AI.
| Feature | Cloud AI (Traditional) | Edge AI (eFabric Powered) |
|---|---|---|
| Response Time | High Latency: Data must travel to the server and back (100ms – 2+ seconds). | Instant Response: Processing happens on-chip (<10ms). |
| Power Consumption | High Power: Requires constant Wi-Fi/LTE radio and high-performance CPUs. | Microwatt Scale: Optimized for battery life; runs on ultra-low power silicon. |
| Connectivity | Dependent: System fails or lags if the internet connection is unstable. | Autonomous: Works 100% offline; no network required for inference. |
| Data Privacy | Lower: Sensitive audio/sensor data is transmitted to third-party servers. | Maximum: Data never leaves the device; processing is strictly local. |
| Operating Cost | Recurring: Monthly cloud processing and data bandwidth fees. | One-Time: Zero cloud costs; intelligence is baked into the hardware. |
| Reliability | External-dependent: Prone to server downtime or network outages. | Resilient: Decoupled from infrastructure; works in remote environments. |
Core Benefits: Privacy, Latency and Bandwidth
By processing data locally on chips like the Syntiant® NDP120, eFabric™ addresses three critical bottlenecks:
- Latency: Detect specific trigger words or phrases with high accuracy and low latency. Since the data doesn't travel to the cloud, the response is nearly instantaneous.
- Bandwidth: There is no need to stream constant audio or sensor data over the network, drastically reducing data costs and infrastructure load.
- Privacy: Because processing happens on-device, sensitive data (like audio from a "listening" device) never leaves the local environment.
The "Always-On" Privacy Standard
eFabric™ enables a new class of "Always-On" intelligence. Devices can remain in a low-power state, continuously monitoring for specific events—such as a glass break or wake word—without recording or transmitting private data.
Only when a trigger is detected does the system activate higher-level processing or communication, maintaining privacy and power efficiency.