AIStorm, an Edge artificial intelligence startup, introduced a family of semi-custom solutions for mobile handsets, IoT, and advanced driver-assistance systems (ADAS) at the MWC 2019 in Barcelona.
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Due to its unique charge-domain processing architecture, AIStorm can outperform digital GPU-based solutions, delivering edge-ready solutions at 5x to 10x lower cost.
AIStorm CEO David Schie sees a huge opportunity to outperform approaches that need to digitize sensor data, a step that introduces latency and the potential to miss important information, said a press release.
“Many industry players are focused on deep submicron GPU/NPU-based solutions to accelerate AI at the edge. We believe that such solutions are not compatible with the real-time processing, power, and low-cost requirements of these applications,” said Schie. “Today, we are reshaping the landscape by enabling complete solutions that include a sensor, an analog front end, and AI processing at low cost, and suitable for even the smallest form factors — without the need to digitize input data.”
AIStorm initially targets two huge markets, mobile/wearable and ADAS:
- AIStorm IoT Vision/IoT Waveform Solutions. Designed for imaging and HID/biometric applications in mobile phones, cameras, wearables & IoT applications, AIStorm offers AI-in-sensor solutions for tasks such as fingerprint sensing, gesture control, heart monitoring and heart-based identification, occupancy sensing, facial recognition, voice input, earbud, drone imaging, image stabilization, and security and intersection cameras.
- AIStorm ADAS Solutions. Designed for advanced driver-assistance systems, AIStorm introduces CIS, SPAD/SiPM and SiGe sensors coupled to its analog AI engines, which eliminate the need for digitization and allow continuous processing of incoming data. Other solutions include gesture control, eye-blink monitoring, voice, and wavelet-based failure detection.
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According to AIStorm, its SoCs are better option because they can process data directly from sensors while it’s still in its native form, eliminating the need to convert data into a digital format. The processed analog data can then be used to train Artificial Intelligence and machine learning models for a wide variety of tasks.
