Innatera shows RISC-V neuromorphic edge AI microcontroller
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Dutch chip startup Innatera has shown its neuromorphic microcontroller for edge AI sensor applications based on the RISC-V open instruction set architecture.
The Spiking Neural Processor T1 is aimed at low power edge AI sensor applications running both neuromorphic and traditional deep neural network AI models.
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Innatera says the chip can deliver energy savings of up to 500x with 100x shorter latency across a range of applications compared to a traditional CPU, DSP or conventional AI accelerator. It has shown demonstrations of radar and audio applications.
The design combines analog-mixed signal neuromorphic computing with a RISC-V processor core and support for accelerating traditional convolutional neural network (CNN) AI models.
Other neuromorphic chips have been developed by Intellisense, SynSense and Renesas, but software support and the availability of models has been a major issue.
To address this, Innatera has developed a software development kit (SDK) for the Spiking Neural Processor called Talamo. This integrates with the PyTorch machine learning framework, providing a streamlined and intuitive platform for model development and training.
The development process also uses the robust visualization and measurement capabilities of Tensorboard. It has a growing library of models with a range of pre-configured models, as well as its comprehensive environment for application development and deployment.
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“Neuromorphic computing is here, and will redefine intelligence at the sensor-edge. We’re excited to unveil the Spiking Neural Processor and announce the availability of the T1 to customers for pre-production trials.”, said Sumeet Kumar, CEO at Innatera.
T1 evaluation kits are available as part of Innatera’s early access program, ahead of its introduction into production later this year.
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