The SoC comprises a digital signal processor (DSP) and the company’s nNetLite neural network (NN) processor, which have both been optimised 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, that allows developers to quickly get next-generation designs to market with their own algorithms, or with DSP Group’s comprehensive and proven suite of optimised 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 emphasising ease of deployment. We look forward to seeing how creatively developers apply the DBM10 platform.”
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.
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 optimisation.
