The Arm Ethos-N processor series delivers the highest throughput and efficiency in the lowest area for Machine Learning inference from cloud to edge to endpoint.

Ethos-N series characteristics

Key features of the Ethos-N family of devices:

  • Tackles a broad spectrum of Machine Learning (ML) requirements, with performance spanning from 1 TOP/s to > 250 TOP/s
  • Scalable multicore technology allows up to eight NPUs to be configured in a tightly coupled cluster, up to 64 NPUs in a mesh configuration
  • Supports multiple neural networks concurrently, for mixed-feature demands enabling new use cases
  • Provides layered security to protect both ML models and input data such as biometrics for financial payments
  • Allows for support and protection of memory and easy handling of multiple users via tight system integration through the ACE-Lite master port and optional SMMU integration
  • Leverages Arm ML software to improve Artificial Intelligence (AI) app portability and provide optimized access to all Arm hardware

Download the Ethos-N datasheet:


Ethos-N comparison table

Key features Performance (at 1GHz)
4 TOP/s 2 TOP/s 1 TOP/s
MAC/Cycle (8x8) 2048 1024
Data types
Int-8 and Int-16
Network support CNN and RNN
Efficient convolution
Winograd support
Sparsity Yes
Secure mode
Multicore capability 8 NPUs in a cluster
64 NPUs in a mesh
Memory system Embedded SRAM 1-4 MB 512 KB 512 KB
Bandwidth reduction Extended compression technology, layer/operator fusion, clustering, and workload tilling
Main interface 1xAXI4 (128-bit), ACE-5 Lite
Development platform Neural frameworks TensorFlow, TensorFlow Lite, Caffe2, PyTorch, MXNet, ONNX
  Neural operator API Arm NN, AndroidNN
  Software components Arm NN, neural compiler, driver and support library
  Debug and profile Layer-by-layer visibility
  Evaluation and early prototyping Arm Juno FPGA systems and cycle models