Overview
The Arm® Cortex®-A5 processor is the smallest, lowest power Arm multi-core processor capable of delivering the internet to the widest possible range of devices: from ultra-low-cost handsets, feature phones and smart mobile devices, to pervasive embedded, consumer and industrial devices.
Both the extremely area and power efficient Cortex-A5 uniprocessor and the scalable Cortex-A5 MPCore multi-core processors are supported by a rich set of features and Armv7 architectural functionality to deliver a high-performance and low-power solution across both application specific and general purpose designs.
The Cortex-A5 processor includes TrustZone® security technology along with a NEON™ multimedia processing engine first introduced with the Cortex-A8 processor. NEON technology is a 128-bit SIMD (Single Instruction, Multiple Data) architecture extension for the Cortex-A series processors, providing flexible and compelling acceleration for intensive multimedia applications.
Learn more about CPU IP core
For the first time in our more than 35-year history, Arm is delivering its own silicon products – extending the Arm Neoverse platform beyond IP and Arm Compute Subsystems (CSS) to give customers greater choice in how they deploy Arm compute – from building custom silicon to integrating platform-level solutions or deploying Arm-designed processors.
As part of the new Arm Lumex compute subsystem (CSS) platform, the Arm C1 CPU cluster – the first built on the Armv9.3 architecture – is the next evolution of our highest performing CPU cluster for consumer devices, designed to unleash the full potential of on-device AI and elevate the user experience.
Unlock ultra-efficient performance, advanced AI processing, and robust security with the Cortex-A320—designed to power the future of IoT and edge AI innovation.
Advanced artificial intelligence (AI), like generative AI, is enhancing all our smart devices. However, a common misconception is that these AI workloads can only be processed in the cloud and data center. In fact, the majority of AI inference workloads, which are cheaper and faster to run than training, can be processed at the edge – on the actual devices.
Steve Raphael, Senior Director, Smartphone Segment Strategy and Go-to-Market, Arm
Saurabh Pradhan, Arm
