CEVA presents its NeuPro-M Heterogeneous and Secure Processor Architecture - Elettronica Plus

CEVA presents its NeuPro-M Heterogeneous and Secure Processor Architecture

Posted 8 febbraio 2022

CEVA announced NeuPro-M, its latest generation processor architecture for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor.

An industry first, NeuPro-M supports both system-on-chip (SoC) as well as Heterogeneous SoC (HSoC) scalability to achieve up to 1,200 TOPS and offers optional robust secure boot and end-to-end data privacy.

NeuPro-M compliant processors initially include pre-configured cores: NPM11 (single NeuPro-M engine) with up to 20 TOPS at 1.25GHz and NPM18 (eight NeuPro-M engines) with up to 160 TOPS at 1.25GHz.

A single NPM11 core, when processing a ResNet50 convolutional neural network, achieves a 5X performance increase and 6X memory bandwidth reduction versus its predecessor, which results in power efficiency of up to 24 TOPS per watt.

Built on the success of its’ predecessors, NeuPro-M is capable of processing all known neural network architectures, as well as integrated native support for next-generation networks like transformers, 3D convolution, self-attention and all types of recurrent neural networks. NeuPro-M has been optimized to process more than 250 neural networks, more than 450 AI kernels and more than 50 algorithms. The embedded vector processing unit (VPU) ensures future proof software-based support of new neural network topologies and new advances in AI workloads.

Furthermore, the CDNN offline compression tool can increase the FPS/Watt of the NeuPro-M by a factor of 5-10X for common benchmarks, with very minimal impact on accuracy.



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