Over the past 20 years, Intel and AMD’s X86 chips have dominated major cloud platforms and enterprise data centers. In addition to the server market, the X86 architecture has also been the leading choice for personal computing devices, including desktop computers. On the other hand, ARM chips have been mainly used in less power-hungry devices like phones, smartwatches, and IoT devices that don’t need massive CPU performance. 

However, recent advancements in ARM architecture are changing the game with ARM chips now capable of running heavy workloads. We are already seeing a significant shift to ARM in the personal computing space. This shift began with Apple’s M-Series chips for Macs and iPads, and it continues with Microsoft’s and Qualcomm’s Snapdragon X-lite chips, which will power the next generation of PCs and tablets. The good news is that these ARM chips offer comparable or even better performance than Intel and AMD’s x86 chips while providing nearly twice the power efficiency.

The efficiency gains and cost-effectiveness of ARM chips have caught the attention of cloud providers. All these major players in the cloud space, including Amazon, Google, and Microsoft have recently been investing heavily in ARM chips and expanding their ARM compute offerings, allowing customers to save on computing costs. Despite the advancements made with ARM chips, they still have a couple of drawbacks that need to be addressed before they finally become the default choice, especially for cloud computing workloads. In this article, we’ll explore ARM chips and their potential to revolutionize cloud computing. We also discuss the challenges that need to be addressed to make this vision of ARM a reality. 

Understanding ARM Architecture

Before getting into the details of using ARM chips for cloud computing, let me walk you through the fundamentals of ARM to ensure everyone is up to speed. For starters, ARM is an acronym for Advanced RISC Machine, which is a family of Reduced Instruction Set Computing (RISC) architectures for computer processors. This simply means that ARM chips use a simpler set of instructions compared to traditional x86 processors. You may think of it as having a smaller toolbox with only the essential tools you need to get the job done.

Arm chips have been around for almost 40 years. However, they have mainly been used in mobile devices and other gadgets where power efficiency is the priority over performance. For instance, all mainstream mobile devices, including the iPhone, iPad, and Android phones, and tablets use ARM-based chips. Due to the recent improvements, ARM chips are also gradually becoming a viable option for much more powerful personal devices, including laptops and desktop computers. 

Key features of the ARM architecture

• RISC Design: ARM processors use a simpler set of instructions compared to Complex Instruction Set Computing (CISC) architectures like x86. This simplicity is the main reason behind their lower power consumption and efficiency.
• Load/Store Architecture: With this architecture, all data processing operations must be performed on data in registers, with separate instructions for loading data from memory and storing data back to memory. This approach helps streamline and speed up the processing operations by keeping the most frequently accessed data within the fast-access registers.
• Conditional Execution: ARM architecture supports conditional execution of instructions, which helps to reduce the number of branch instructions and improve performance.
• Pipeline Design: ARM processors often use pipelining, where multiple instruction phases are overlapped, allowing the processor to execute instructions more efficiently.

x86 Architecture vs ARM

Some of the key differences between ARM and traditional X86 CPUs include. 

• Instruction Set: ARM uses a RISC architecture with a smaller, simpler set of instructions, whereas x86 uses a CISC (Complex Instruction Set Computing) architecture with a larger, more complex set of instructions.
• Power Consumption: ARM processors are designed for energy efficiency, making them ideal for mobile devices and energy-sensitive applications. x86 processors, while powerful, typically consume way more power.
• Performance: x86 processors generally offer higher single-threaded performance and are optimized for desktop and server applications. ARM processors provide excellent performance for multi-threaded, parallel workloads. However, ARM chips have been becoming increasingly competitive in single-threaded performance in the last couple of years.
• Software Ecosystem: x86 has a long-established ecosystem with broad software support, particularly for legacy applications. ARM’s ecosystem is growing rapidly, with increasing support from major operating systems and software developers. 

Evolution of ARM in Cloud Computing

Adapting ARM architecture for cloud computing has faced several hurdles over the years, including software compatibility, lack of enterprise-grade ARM servers, and skepticism about ARM’s performance for server workloads. However, due to the many breakthroughs in recent years, the future of ARM for cloud computing is starting to look bright. 

Let’s explore some of these breakthroughs: 

Performance Improvements

ARM processors have made substantial strides in performance, narrowing the gap with x86 processors traditionally favored for high-performance computing. Advances in ARM’s microarchitecture, such as improved pipeline efficiency, larger cache sizes, and enhanced instruction sets, have boosted their ability to handle compute-intensive tasks effectively. For instance, the latest AWS Graviton ARM chips outperform comparable X86 intel chips by up to 40% in some workloads.

Specialized Workloads and Accelerators

ARM has integrated specialized accelerators and features into its processors to cater to emerging workloads such as artificial intelligence (AI), machine learning (ML), and digital signal processing (DSP). These enhancements improve performance for tasks that require parallel computing, vector processing, or real-time data analysis. For instance, Apple is using M-Series chips to power its Private Compute Cloud servers for Apple Intelligence features coming to the iPhone, iPad, and Mac later this year. This is all thanks to the integrated neural engine cores that are specifically designed to handle AI tasks. 

Support and Ecosystem Growth

One of the major stumbling blocks for the adoption of ARM chips has been the lack of software support. However, ARM has expanded its ecosystem significantly, collaborating with software developers, cloud providers, and Operating System Vendors like Microsoft to optimize applications and tools for ARM-based platforms. For instance, all the major server Operating Systems, including Windows Server and Ubuntu Server already have ARM versions. This support ensures compatibility and performance optimization across a wide range of software and services. 

Advancements in Design and Manufacturing Processes for ARM

ARM chip manufacturers like TSMC continue to innovate with each new generation of processors, introducing advanced technologies like 5nm, 4nm, and even 3nm fabrication processes. These advanced fabrication technologies further improve their energy efficiency and performance per core. Other improvements in ARM include more memory bandwidth and enhanced security features. 

All Major Cloud Providers Have ARM Compute Options

If you’re looking to leverage the benefits of ARM in the cloud, you’re in luck. All major cloud providers now offer ARM computing options for both standard and serverless computing. Here is a breakdown of the choices available to you from these providers: 

• Amazon Web Services (AWS): AWS offers ARM-based instances powered by Graviton processors. Graviton processors are designed for general-purpose, compute-optimized, and memory-optimized workloads. Some of the instance options available include A1, M6g, C6g, and R6g instances. Graviton is also available for Lambda functions (serverless) in some regions. 
• Microsoft Azure: Azure provides ARM-based VMs powered by Azure Cobalt and Ampere processors as part of its offering. Some examples of ARM-powered instance types on the Azure platform include Dpsv5 and Epsv5 VMs.
• Google Cloud Platform (GCP): Google Cloud also offers ARM-based instances powered by Axion – their next-generation in-house ARM-based chip.
• Oracle Cloud Infrastructure (OCI): Oracle provides ARM-based instances using Ampere Altra processors, focusing on performance and cost efficiency.
• Alibaba Cloud: Alibaba Cloud also offers ARM-based instances powered by the Yitian 710 processor, designed for high performance and efficiency.

Use Cases of ARM chips in the cloud.

Some of the common use cases for ARM chips on platforms like AWS and Google Cloud include: 

• Web Servers: ARM-based instances are beginning to be widely preferred for hosting web servers due to their cost-effectiveness and energy efficiency. For instance, companies deploying Graviton instances experience improved server density and reduced operational costs per request. 
• Machine Learning: ARM processors, particularly Graviton3, have demonstrated substantial performance gains for machine learning inference tasks. Organizations using Graviton3 for AI workloads report up to 40% cost savings and faster inference times compared to traditional x86 instances. Such improvements are starting to make ARM-based instances a preferred choice for deploying AI models in production environments, where efficiency and cost-effectiveness are critical factors.
• Serverless Computing: ARM-based instances are also well-suited for serverless architectures. For instance, AWS Lambda functions running on Graviton 2&3 instances show faster execution times and reduced latency, enhancing responsiveness and scalability for serverless applications. 
• Video Streaming and Content Delivery: ARM-based instances are ideal for video streaming and content delivery networks (CDNs) because of their ability to handle high volumes of data with reduced energy consumption. Companies such as Netflix are already leveraging ARM instances for streaming services to experience improved reliability and reduced costs per viewer. 
• IoT and Edge Computing: ARM-based instances are also well-suited for IoT and edge computing applications due to their low power consumption and compact form factor. Organizations deploying ARM in edge environments benefit from reduced latency and improved data processing capabilities at the edge. For instance, smart city initiatives use ARM-based edge devices to collect and process sensor data in real-time. 
• Data Analytics and Big Data Processing: ARM processors offer significant advantages for data analytics and big data processing tasks due to their efficiency, scalability, and specialized optimizations. 

Popular Brands Using ARM for Their Cloud Workloads

• Snap: Snap Inc., the parent company of Snapchat, migrated a significant portion of its infrastructure to AWS Graviton instances. The platform uses EC2 C6g and M6g instances with Amazon EKS to run its messaging core service. 
• X (former Twitter): X also uses AWS Graviton EC2 instances to optimize costs and enhance performance for various cloud services. 
• Datadog: Datadog, a leading monitoring and analytics platform, adopted AWS Graviton instances for its cloud infrastructure to pass some cost savings to its customers by offering more features at slightly lower prices. 
• Pinterest: The social media platform moved a significant percentage of its workloads to AWS Graviton instances, leading to an overall reduction in computing costs. 
• Zendesk: After migrating its workloads to AWS Graviton, the platform improved performance by 30% and reduced its computing costs by 42% per month.

Challenges of ARM in the Cloud

While ARM architecture offers many benefits in cloud computing, there are several challenges that organizations may face when adopting ARM-based instances and serverless computer options. Let’s explore these challenges and their potential solutions: 

Software Compatibility

Many existing applications and software libraries are optimized for x86 architecture. Migrating these applications to ARM may require significant effort, including refactoring code to ensure compatibility and optimal performance. However, developers can take advantage of software development kits (SDKs) provided by platform vendors to make the process of writing and refactoring their code for arm much easier and faster.  

Optimization Efforts

ARM processors may require different optimization strategies compared to x86 processors. Organizations might need to invest time and resources in optimizing their applications to fully take advantage of the benefits of the ARM architecture. The good news is that all the major cloud providers often offer tools and documentation to assist in this optimization process.

Ecosystem Maturity

Even though the ARM ecosystem has been growing in recent years, it is not yet as mature or extensive as the x86 ecosystem, especially for enterprise-level applications and services. Of course, this will likely change in the next couple of years, with continued collaboration between ARM, cloud providers, and the developer community. 

Performance Variability

Performance characteristics of ARM-based instances can vary depending on the specific workload. Some applications may not see the same level of performance improvement as others. Before migrating to ARM computer options, it is crucial to conduct thorough testing and benchmarking to understand how your specific workloads perform on ARM-based instances. 

Limited Hardware Options

While ARM processors are increasingly prevalent in the market, they may not offer the same range of specialized hardware components like GPUs or TPUs (Tensor Processing Units) as x86 architectures do. This limitation can impact users and organizations that rely on these specialized hardware accelerators for tasks such as high-performance computing, machine learning, or graphics rendering. It will take time for ARM chip makers to create X86 equivalents for such tasks.

The Future of ARM In the Cloud

Even though X86 architecture has dominated the cloud space for decades, ARM’s superior power efficiency, lower costs, and increasing performance make it a compelling alternative for many enterprise customers. Leading cloud providers are also actively investing in ARM-based offerings for both traditional and serverless computing options. We are also seeing significant advancements in software compatibility and workload optimization to address past challenges.

Despite the many challenges of using ARM in the cloud, the future looks bright. Businesses seeking to optimize costs, improve efficiency, and embrace a more sustainable cloud infrastructure will find ARM chips compelling. As the ARM ecosystem continues to mature and hardware options expand, we can expect ARM to play an increasingly prominent role in shaping the future of cloud computing.