Accelerate Your GenAI, AI and ML Workloads on Arm CPUs

Optimize ML inference and training performance on AWS, as well as best practices for ML inference using PyTorch 2.0, and more.

Best Practices to Optimize ML Performance on AWS Graviton

• Improve ML performance on AWS Graviton: A series of blogs covering how to improve performance and reduce costs for ML inference, as well as NN training, and more.
• Migrating to Arm – 1.8x faster Deep Learning Inference workloads in AWS Graviton3: A case study that compares the ML inference performance with x86, achieving 1.8x faster inference workloads.

Optimizing Inference Performance with PyTorch 2.0

• Example tutorial showcasing how to achieve the best inference performance with bfloat16 kernels, and the right back-end selection.

Docker Images for TensorFlow and PyTorch on Arm

• Learn how to build and use Docker images for TensorFlow and PyTorch for Arm.

Accelerate your AI/ML framework, tools, and cloud services with open-source Arm libraries and optimized Arm SIMD code.

Accelerating PyTorch 2.0 Inference with AWS

• A collaboration between AWS, Arm, and Meta to optimize the performance of PyTorch 2.0 inference for Arm-based processors, increasing performance up to 3.5 times compared to the previous PyTorch release, and more.

Arm Compute Library (ACL)

• ACL is an open-source fully featured library, with a recollection of low-level ML functions optimized for Arm Neoverse and other Arm architectures.

Arm Kleidi

• Arm Kleidi open-source libraries are a lighter weight performance library (compared to ACL) for accelerating AI and ML workloads and frameworks.

Arm KleidiCV

• The Arm KleidiCV library is designed for image processing and integrates into any CV framework to enable best performance for CV workloads on Arm.

Arm SIMD code

• Optimize your AI/ML workloads with Arm SIMD code, either in assembly or using Arm Intrinsics in C/C++, to leverage huge performance gains.