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Ceva NeuPro Accelerates Neural Nets

January 30, 2018

Author: Mike Demler

Ceva has extended its line of neural-network-processor intellectual property (IP) by launching NeuPro, which comprises a family of cores optimized for general-purpose machine learning. It previously developed the XM-series of deep-learning accelerators (DLAs), but whereas they use an architecture optimized for computer vision (CV), the NeuPro ISA supports any machine-learning application and doesn’t require a separate host-CPU controller.

The NeuPro employs two separate cores: the DSP-based NeuPro Vector Processor Unit (VPU), and the NeuPro Engine. The VPU plays more of a supervisory role while offloading most of the computation to the NeuPro Engine. Although the company withheld details, the VPU is similar to its previous DSP-based CV cores. It includes a scalar unit and vector unit that run the deep-neural-network (DNN) control code, and it’s programmable for handling layer functions that the NeuPro Engine omits.

The NeuPro Engine is the new element in Ceva’s architecture. Whereas the XM6 uses the vector DSP to run the normalization, pooling, and other layers in a convolutional neural network (CNN), NeuPro shifts those functions to specialized hardware in the NeuPro Engine. The XM6 employs a separate accelerator that only runs CNN multiplier-accumulate (MAC) functions, but the NeuPro Engine includes hardware that accelerates all layers. Compared with its predecessor, the new design offers a more streamlined data flow that reduces external-memory transactions, which often bottleneck neural-network performance. Keeping the DNN data local to the execution units also saves power.

NeuPro’s target applications include advanced driver-assistance systems (ADASs), augmented-reality (AR) headsets, drones, smartphones, and surveillance cameras. The IP will become available to lead customers in 2Q18; the company plans a general release in 3Q18. 

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