Semiconductor maker Renesas is partnering with deep learning chip technology firm Syntiant to develop voice-controlled multimodal AI solution that enables low-power contactless operation for image processing in vision AI-based IoT and edge systems, such as self-checkout machines, security cameras, and video conference systems, and smart appliances such as robotic cleaning devices.
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The new solution combines the Renesas RZ/V Series vision AI microprocessor unit (MPU) and the low-power multimodal, multi-feature Syntiant NDP120 Neural Decision Processor to deliver advanced voice and image processing capabilities. The joint solution features always-on functionality with quick voice-triggered activation from standby mode to perform object recognition, facial recognition, and other vision-based tasks that are critical functions in security cameras and other systems. For example, while user-defined voice cues drive activation and system operation, vision AI recognition tracks operator behavior and controls operation or issues a warning when suspicious actions are detected.
The multimodal architecture makes it easier to create contactless user experiences for vision AI-based systems. Using a dedicated, power-efficient chip for voice recognition reduces standby power consumption while speeding up system development because it is possible to develop software independently of the vision AI functionality, reports Renesas.
“We anticipate that demand for multimodal systems that use multiple streams of input information – both image and voice – will increase moving forward as a way to improve both ease of use and safety,” said Hiroto Nitta, Senior Vice President and Head of SoC Business in the IoT and Infrastructure Business Unit at Renesas. “Through the collaboration between Renesas, a leader in low-power image AI technology, and Syntiant, a leader in voice AI technology, we will accelerate the adoption of low-power, ultra-small smart voice AI technology in embedded systems and deliver new combined solutions to customers globally.”
“Voice-based user interfaces will make it possible for customers to deliver new user experiences that bring the next generation of innovative ideas from concept to reality, said Syntiant CEO Kurt Busch. “We’ve already shipped more than 15 million of our deep learning NDPs globally to enable always-on voice in a wide variety of consumer and industrial IoT applications. Our collaboration with Renesas delivers a powerful, low-power voice and image solution that is certain to accelerate traction among a global customer base in a variety of devices and use cases.”
The Renesas RZ/V Series MPU for vision AI incorporates Renesas’ exclusive DRP-AI (Dynamically Reconfigurable Processor-AI) accelerator and combines high-precision AI inference with a power efficiency that is among the best in the industry. This superior power performance eliminates the need for heat dispersion measures such as heat sinks or cooling fans, which reduces the bill of materials (BOM) cost and makes it possible to integrate vision AI into a wide range of embedded applications.
The Syntiant NDP120 chip incorporates sophisticated AI capabilities that can be used to implement many high-precision, hands-free voice functions, including speaker recognition, keyword detection, multiple wake words, and local command recognition. Packaged with the Syntiant Core 2 neural network inference engine, the NDP120 can also run multiple applications simultaneously while minimizing power consumption to 1mW battery power.
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The new voice-controlled multimodal AI solution uses multiple mutually compatible devices from broader Renesas portfolio to provide customers an elevated prototyping platform for faster time to market and reduced risk. The new solution is part of Renesas’ Winning Combinations, which feature compelling analog, power, and embedded processing product combinations that help customers accelerate their designs and get to market faster.
Availability
The reference design for the new multimodal AI solution is available now, including circuit diagrams and BOM lists.
