Syntiant Neural Decision Processors

Organization
Syntiant · United States
Solution Type
Ready Product / Solution
Collaboration
Business Collaboration (Joint Venture), Licensing

1. Technology overview

Syntiant is a Physical AI company founded in 2017 and headquartered in Irvine, California, per its October 2026 press release. It supplies processors, sensors and AI models for always-on natural user interfaces in earbuds, wearables, industrial systems and vehicles.

The Neural Decision Processors (NDP) run deep learning on-device at ultra-low power, supported by audio, speech, sensor and vision models, SDKs and training tools, plus MEMS microphones and vibration sensors. In October 2026 the NDP120 and six SiSonic MEMS microphones were announced inside the STEPX Neo agentic phone from Shanghai-based STEPX.

2. Highlighted features & specifications

  • Neural Decision Processors: NDP chips that run deep learning on-device at ultra-low power.
  • AI models: hardware-agnostic audio, speech, sensor and vision models.
  • Tools and SDKs: dataset curation, model compression and optimization.
  • MEMS sensors: SiSonic microphones and vibration sensors.
  • Physical AI platform: NDP120 with six SiSonic microphones for always-on hearing in the STEPX Neo phone.
  • Application areas: personal devices, smart home, healthcare, automotive, robotics and industrial.

3. Potential application

Earbud and wearable contract manufacturers can add keyword and event detection using NDP chips. Smart-home and security device makers can use audio detection models on NDP chips.

4. Benefits

Always-on audio and sensing at ultra-low power lets the main processor wake less often. Chips, sensors and models come from one vendor, so device makers can source the whole sensing chain. Hardware-agnostic models and SDKs run on platforms from MCUs to GPUs. Company materials report tens of millions of NDPs and ML models deployed.

6. Other information

Founded 2017 and headquartered in Irvine, California per the press release. Products span NDP chips, ML models and SiSonic MEMS microphones.

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