AmpereOne is the next-gen Cloud Native arm-based processors by Ampere, with a core range from 136 to 192 cores. AmpereOne surpasses Ampere’s previous generation of cloud native processors Ampere Altra and Ampere Altra Max, which has the highest core count of 128 cores. These cores run on upto 3.0 GHz based on Armv8.6+ instruction set architecture. It comes in 5 custom core counts: 136 cores, 144 cores, 160 cores, 176 cores, 192 cores. AmpereOne processors are solely focused on CSPs (Cloud service providers).

Each core has private caches with 2 MB of L2 cache and 16 KB of L1 instruction cache alongside 64 KB of L1 data cache. With a substantial 64 MB system-level cache, it ensures swift data access. Memory-wise, it supports up to 8 channels of DDR5 with ECC, accommodating up to 16 DIMMs and a total memory capacity of 8 TB. It provides excellent connectivity with 128 lanes of PCIe Gen5 and optional 64-lane CCIX for multi-socket setups. Its features include interrupt and IO virtualization, enterprise-grade RAS, coherent mesh interconnect, and cloud-native capabilities. Nested virtualisation and Mesh Congestion Mgmt provides perfomance consistency and single-key memory encryption with memory tagging enhances the security.

Features of AmpereOne Processors:

• Directory-based snoop filters and 64 distributed home nodes enable smooth connectivity across cores via a coherent mesh network. QOS enforcement and mesh congestion control improve the predictability of data movement.

• It offers a safe microarchitecture for multi-tenant cloud settings and reduces the problems caused by noisy neighbours.

• The AmpereOne series of processors offers scalable computing solutions, particularly for hyperscale data centres, with industry-leading power efficiency per core.

• Up to 8 TB of memory accessible space and enhanced memory bandwidth are provided by the family’s eight 80-bit DDR5 channels.

• It supports linear workload scaling and consistent incremental performance per core.

• It allows for flexible system configurations by supporting up to 128 PCIe Gen5 lanes per socket.

• It reduces space, power requirements, and carbon footprints while leading the industry in performance per rack.

AmpereOne processors represent a significant advancement in cloud-native computing, offering up to 192 cores, enhanced connectivity, and industry-leading power efficiency. Designed specifically for cloud service providers, they prioritize scalability, reliability, and energy efficiency, making them ideal for hyperscale data centers.

ARM (Advanced RISC Machines) is a set of RISC instruction set architectures for computer processors, developed by Arm Ltd. It’s widely used due to its affordability, energy efficiency, and suitability for portable devices like smartphones, laptops, and other embedded systems.

ARM processors aren’t just for portable devices; they’re also used in desktops and servers, with Fugaku, the world’s fastest supercomputer from 2020 to 2022, powered by ARM processors. The first ARM processor released in 1990 was a collaborative effort by Acorn and Apple computer.

ARM uses RISC (Reduced Instruction Set Computer) computer architectuere that was first developed by Acorn in 1985. RISC architecture is designed to simplify the instructions that are given to the computer. Compared to CISC (Complex Instruction Set Computer), which requires less albiet instructions for a processor to perform a certain task and thus less code, while RISC architecture requires more simple instructions to perform the same task, thus requiring more code. ARM works by executing a single instructions per clock cycle, engaging less transistors from the hardware than complex instructions.

Features of ARM processors:

• Multiprocessing System: A multiprocessing system utilizes multiple processors to execute tasks simultaneously, boosting overall system performance.
• Tightly Coupled Memory: Tightly coupled memory refers to a configuration where processors share access to a common memory space, enabling fast communication and data sharing between them.
• Memory Management: Memory management involves organizing and optimizing the allocation, usage, and deallocation of memory resources within a computer system to ensure efficient operation and prevent memory-related issues.
• Thumb-2 Technology: Thumb-2 technology is a feature commonly found in ARM processors that enhances code density and performance by providing a mix of 16-bit and 32-bit instructions.
• One-Cycle Execution Time: Achieving one-cycle execution time means completing the execution of an instruction within a single clock cycle, maximizing processor throughput and efficiency.
• Pipelining: Pipelining is a technique used in processor design to overlap the execution of multiple instructions, improving throughput and overall performance by allowing multiple instructions to be in different stages of execution simultaneously.
• A Large Number of Registers: Having a large number of registers in a processor enables efficient execution of instructions by providing ample storage for intermediate values and reducing the need for frequent memory access, thus enhancing performance.

In conclusion, ARM processors are extremely energy efficient and suitable processors for a variety of computer devices. These processors are not capable of high performance. These processors also need highly skilled programmers because execution is very important for them since the entire performance of the processor depends on the execution. There are a lot of compatibility issues with these processors because some software may not be compatible with them.

Asymmetric multiprocessing (AMP or ASMP) systems have different types of CPUs, some with special roles like executing operating system code or handling I/O operations. Before symmetric multiprocessing (SMP) became widely available, AMP was the go-to method for managing multiple CPUs. It’s also used to offer cheaper alternatives on systems with SMP capability. AMP systems vary in how they distribute tasks among CPUs, making some more specialized for specific task than others. AMP architecture elevates system’s performance and capabilities because the processing resources are distributed in a more efficient way.


Processing cores are stratified in AMP architecture in such a way that high-performance cores are consumed by compute intensive workloads such as big data processing, while workloads that do not require much computing resources are handled by low-performance cores. This ensures that least resources are consumed while maintaining efficiency. AMP systems are often used in embedded systems, such as smartphones, IoT devices, and consumer electronics, where specialized processors handle specific tasks like graphics processing, audio processing, or communication.

Benefits of AMP architecture:

• AMP boosts performance by assigning tasks to specific cores, cutting down response times and making critical apps run smoother.
• It saves power by spreading tasks across different cores, making devices more energy-efficient and giving longer battery life.
• With AMP, devices can handle multiple tasks at once because different cores handle different tasks simultaneously.
• Manufacturers can make devices cheaper by using a mix of powerful and low-power cores in one chip, giving you more bang for your buck.

Heterogeneous Computing

Heterogeneous computing takes asymmetric multiprocessing a step futher by incorporating different kinds of coprocessors and accelerators with specialised processing capabilities. Heterogeneous systems utilize specialized accelerators for tasks like scientific simulations and machine learning, boosting performance significantly. They offer versatility across various applications and environments, optimized energy usage, and scalability through the addition of more accelerators to accommodate increasing workloads efficiently.

In conclusion, the AMP architecture stands out as an exceptionally efficient and resourceful processing design. However, it does come with its own set of drawbacks. Developing software for these architectures is a complex task, as task scheduling and load balancing across different components require advanced algorithms and techniques.