Deploying AI models on local devices (phones, IoT, embedded systems) rather than cloud servers, enabling low-latency inference and data privacy.
AI that runs right on your phone or device instead of needing the internet — so it works even when you're offline.
Running AI models directly on devices like phones, cars, or cameras instead of sending data to the cloud. It's faster and more private.
Deploying AI models on local devices (phones, IoT, embedded systems) rather than cloud servers, enabling low-latency inference and data privacy.
On-device inference using quantized, distilled, or purpose-built models optimized for constrained compute environments — trading model size for latency, privacy, and offline capability.
Inference at the network edge using hardware-aware model optimization (quantization, pruning, knowledge distillation, neural architecture search) — targeting NPU/DSP accelerators with constraints on power, memory, and thermal envelope.
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