DSP Group, a specialist provider of wireless and voice-processing chipset solutions for converged communications, announced the DBM10, a low-power, cost-effective artificial intelligence (AI) and machine learning (ML) system on chip (SoC).
The SoC comprises a digital signal processor (DSP) and the company’s nNetLite neural network (NN) processor, both optimized for low-power voice and sensor processing in battery operated devices. This architecture offers developers full flexibility of partitioning innovative algorithms between DSP and NN processor and enables fast TTM for integration of voice and sensing algorithms such as noise reduction, AEC, wake-word detection, voice activity detection and other ML models.
The DBM10 features an open platform approach with a comprehensive software framework. This allows developers to quickly get next-generation designs to market with their own algorithms, or with DSP Group’s comprehensive and proven suite of optimized algorithms for voice, sound event detection (SED), and sensor fusion for applications ranging from true wireless stereo (TWS) headsets to smartphones, tablets, wearables, and IoT.
“Edge applications for AI are many and diverse, but almost all require the ultimate in terms of low power, small form factor, cost effectiveness, and fast time-to-market, so we are very excited about what the DBM10 brings to current and new customers and partners,” said Ofer Elyakim, CEO of DSP Group.
“Our team has worked to make the absolute best use of available processing power and memory for low-power AI and ML at the edge—including developing our own patent-pending weight compression scheme—while also emphasizing ease of deployment. We look forward to seeing how creatively developers apply the DBM10 platform.”
“SoCs with the appropriate processors for critical functions are key to enabling innovative products with acceptable battery life,” said Phil Solis, Research Director at IDC. “Such SoCs, with audio and sound DSPs and NPUs, need to support common frameworks and standards to enable competitive voice- and other sound-enabled smart products across end-user and IoT devices.”
The DBM10 adds to DSP Group’s SmartVoice line of SoCs and algorithms that are deployed globally in devices ranging from smartphones and laptops/PCs, to set-top boxes, tablets, remote controls, and smart IoT devices for the home. SmartVoice last year reached the 100 million units shipped milestone, so the low-power DBM10 is supported by an established ecosystem of third-party algorithm providers. Key partners have integrated already their NN algorithms on the nNetLite NN processor at the heart of the DBM10 to achieve maximum performance at the lowest power consumption.
Working alongside a programmable low-power DSP, the nNetLite processor supports all standard deep NN (DNN) and ML frameworks and employs a comprehensive cross-platform toolchain for model migration and optimization.
Key specifications include:
* A highly-compact form factor: ~4 mm2
* Ultra-low-power inference consume~500 µW for voice NN algorithms
* Runs Hello Edge 30-word detection model @ 1 MHz (125 MHz available)
* Allows porting of large models (10s of megabytes) without significant accuracy loss using model optimization and compression.
Hordon Kim
International Editor, hordon@powerelectronics.co.kr
